Master machine learning fundamentals including supervised and unsupervised learning, regression, classification, clustering, and model evaluation from top providers.
A concise, hands-on course on the fundamentals of synthetic data, its applications, and key generation techniques including statistical methods and generative AI approaches like GANs and VAEs.
Master the fundamentals of machine learning with this comprehensive course from Stanford University
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A comprehensive course on Udemy that covers building, training, and deploying machine learning models using Microsoft Azure ML Studio, including no-code and Python-based approaches. It covers AutoML as a key component of the Azure ML platform.
This course focuses on the fundamentals of Data Science, Machine learning, and deep learning in the beginning and with the passage of time, the content and lectures become advanced and more practical. But before everything, the introduction of python is discussed. Python is one of the fastest-growing programming languages and if we specifically look from the perspective of Data Science, Machine learning and deep learning, there is no other choice then “python” as a programming language.First of all, there is a crash course on python for those who are not very good with python and then there is an exercise for python that is supposed to be solved by you but if you feel any difficulty in solving the exercise, the solution is also provided.Then we moved on towards the Data Science and we start from data parsing using Scrapy then the data visualizations by using several libraries of python and finally we end up learning different data preprocessing techniques. And in the end, there is a complete project that we’ll do together.After that, we’ll be learning a few classical and a few advanced machine learning algorithms. Some of them will be implemented from scratch and the others will be implemented by using the builtin libraries of python. At the end of every algorithm, there will be a mini-project.Finally, Deep learning will be discussed, the basic structure of an artificial neural network and it’s the implementation in TensorFlow followed by a complete deep learning-based project. And in the end, some hyperparameter tuning techniques will be discussed that’ll improve the performance of the model.About The Instructor:Below is an introduction to Mr. Sajjad Mustafa, the instructor of this course.He an expert in Web
Welcome to the Advanced Machine Learning & Deep Learning Masterclass 2024! This comprehensive course is designed for both business professionals and researchers, offering over 24 hours of in-depth video content. Whether you're new to Python programming or experienced in the field, this course equips you with essential machine learning and deep learning techniques, from foundational Python skills to advanced neural network architectures.What You Will Learn:Python for Machine Learning: Set up the environment, use popular tools like Anaconda and PyCharm, and learn Python basics through step-by-step tutorials.Data Understanding & Preprocessing: Dive deep into statistical analysis, data pre-processing techniques, feature selection, and data visualization with Python.Artificial Neural Networks: Build neural networks from scratch, explore deep learning frameworks like Keras, and implement a full deep learning project on handwritten digit recognition.Advanced Deep Learning Mastery: Go beyond the basics with comprehensive modules on Convolutional Neural Networks (CNNs), transformers, large language models, and deep generative models. You'll learn how to construct and train models that power today’s AI innovations, including reinforcement learning and sequence models.Naive Bayes Classifier & NLP: Learn the fundamentals of Naive Bayes classification and explore natural language processing, including tokenization, part-of-speech tagging, and real-world NLP projects.Linear & Logistic Regression: Master regression models with hands-on demos for univariate and multivariate scenarios.With practical hands-on demos, coding exercises, and real-world proj
Atenção! Nas aulas deste curso é utilizada a versão 1.x do TensorFlow, sendo possível acompanhar as aulas utilizando essa versão. Adicionalmente, disponibilizamos o código atualizado considerando a versão 2.x. Em breve pretendemos regravar todas as aulas deste cursoA área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, eBay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o
Welcome to my " Complete Python for Data Science & Machine Learning from A-Z " course.Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Do you want to learn one of the employer’s most requested skills? If you think so, you are at the right place. Python, machine learning, Django, python programming, machine learning python, python Bootcamp, coding, data science, data analysis, programming languages.We've designed for you "Complete Python for Data Science & Machine Learning from A-Z” a straightforward course for the Complete Python programming langu
Welcome to this Deep Learning Image Classification course with PyTorch2.0 in Python3. Do you want to learn how to create powerful image classification recognition systems that can identify objects with immense accuracy? if so, then this course is for you what you need! In this course, you will embark on an exciting journey into the world of deep learning and image classification. This hands-on course is designed to equip you with the knowledge and skills necessary to build and train deep neural networks for the purpose of classifying images using the PyTorch framework.We have divided this course into Chapters. In each chapter, you will be learning a new concept for training an image classification model. These are some of the topics that we will be covering in this course:Training all the models with torch.compile which was introduced recently in Pytroch2.0 as a new feature.Install Cuda and Cudnn libraires for PyTorch2.0 to use GPU. How to use Google Colab Notebook to write Python codes and execute code cell by cell.Connecting Google Colab with Google Drive to access the drive data.Master the art of data preparation as per industry standards. Data processing with torchvision library. data augmentation to generate new image classification data by using:- Resize, Cropping, RandomHorizontalFlip, RandomVerticalFlip, RandomRotation, and ColorJitter.Implementing data pipeline with data loader to efficiently handle large datasets.Deep dive into various model architectures such as LeNet, VGG16, Inception v3, and ResNet50.Each model is explained through a nice block diagram through layer by layer for deeper understanding.Implementing the training and Inferencing pipeline.Understanding transfer learning to train models on less data.Display the model inferencing result
Welcome to the Full Stack Data Science & Machine Learning BootCamp Course, the only course you need to learn Foundation skills and get into data science.At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:The course is taught by the lead instructor at the PwC, India's leading in-person programming bootcamp.In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.To date, I’ve taught over 10000+ students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.In the curriculum, we cover a large number of important data science and machine learning topics, such as:<
Learn AWS Machine Learning and AI Complete Course
This course provides a comprehensive understanding of the theory behind Support Vector Machines, including the derivation of Linear SVM, the Kernel SVM using Lagrangian Duality, and the application of Quadratic Programming. It covers practical applications like image recognition and spam detection.
Python est reconnu comme l'un des meilleurs langages de programmation pour sa flexibilité. Il fonctionne dans presque tous les domaines, du développement Web au développement d'applications financières. Cependant, ce n'est un secret pour personne que la meilleure application de Python est dans les tâches d'apprentissage automatique, d'apprentissage en profondeur et d'intelligence artificielle.Bien que Python facilite l'utilisation du Machine Learning et du Deep Learning, il sera toujours assez frustrant pour quelqu'un qui n'a aucune connaissance du fonctionnement de l'apprentissage automatique.Si vous connaissez les bases de Python et que vous avez envie d'apprendre le Deep Learning, ce cours est fait pour vous. Ce cours vous aidera à apprendre à créer des programmes qui acceptent la saisie de données et automatisent l'extraction de fonctionnalités, simplifiant ainsi les tâches du monde réel pour les humains.Il existe des centaines de ressources d'apprentissage automatique disponibles sur Internet. Cependant, vous risquez d'apprendre des leçons inutiles si vous ne filtrez pas ce que vous apprenez. Lors de la création de ce cours, nous avons tout filtré pour isoler les bases essentielles dont vous aurez besoin dans votre parcours d'apprentissage en profondeur.C'est un cours de base qui convient aussi bien aux débutants qu'aux experts. Si vous êtes à la recherche d'un cours qui commence par les bases et passe aux sujets avancés, c'est le meilleur cours pour vous.Il enseigne uniquement ce dont vous avez besoin pour vous lancer dans l'apprentissage automatique et l'apprentissage en profondeur sans fioritures. Bien que cela aide à garder le cours assez concis, il s'agit de tout ce dont vous avez besoin pour commencer avec le sujet.
Learn Complete Machine Learning & Data Science Bootcamp 2025
Why this Course?Lot of us might have experienced difficulty when relating Machine Learning and Deep Learning models. This course aims to answer usual doubts such as,Why Deep Learning?Why Neural Network performs better than Machine Learning models?Deep Learning and Machine Learning are totally different technologies or they are much related?How Deep Learning evolved from Machine Learning?What it Covers?The course covers Machine Learning models such as Linear Regression, Perceptron, Logistic Regression and a Deep Learning model Dense Neural Network. The four chapters (videos) of the course deal with the adult life of a Legend named Mr. S and show how he used the Machine Learning and Deep Learning models to solve interesting problems such as partying, dating, searching for soulmate and eventually marrying the suitable girl in his life. Through the journey of Mr. S, you will finally get to know why Neural Network performs better & how Machine Learning and Deep Learning are related. Videos contain interesting scenarios with simple numerical examples and explanations.Who can opt for this Course?This course will be highly useful for those individuals,Who does/doesn't have CS background and wants to understand Deep Learning technically without coding & too much mathematics.Who are getting started with Machine Learning or Deep Learning.Who seeks the answer: Why Neural Network perform better than Machine Learning models and how Deep Learning evolved from Machine Learning.Who does research AI and have fundamental doubts about functionality of Neural Networks.
This course provides practical skills in using Python and the scikit-learn library for machine learning, with a focus on supervised learning.
Learn Machine Learning and AI Foundations: Value Estimations
Si estás buscando un curso práctico, completo y avanzado para aprender Machine Learning y Data Science con Big Data utilizando PySpark, has venido al lugar correcto.Este curso está diseñado para aprender todo lo relacionado con el Machine Learning y Data Science en Spark como modelos de aprendizaje automático de clasificación, regresión, clustering, NLP, Pipelines y técnicas para la ingeniería de datos y preprocesamiento. También te enseñaremos a programar en PySpark y las buenas prácticas para trabajar con Big Data, visualización de datos o analítica avanzada. Finalmente, aprenderás las últimas tecnologías que han permitido impulsar el Machine learning con Spark como MLFlow, Databricks, Spark ML o Spark Koalas.Este curso es para científicos de datos o aspirantes a científicos de datos que desean obtener capacitación práctica, con las últimas tecnologías y aplicable al mundo real en PySpark (Python para Apache Spark)El Big Data ha revolucionado el campo del Machine Learning, permitiendo entrenar modelos sobre grandes cantidades de datos. El Machine Learning convencional con Python se ha quedado obsoleto y nuevas tecnologías como Apache Spark han cobrado gran relevancia. Este curso te enseñará todo lo que necesitas saber para posicionarte en el mercado laboral del Machine Learning y aprenderás una de las habilidades más demandadas para ingenieros de datos y científicos de datos.En este curso te enseñaremos todas las habilidades de Machine Learning con PySpark, partiendo desde las bases hasta las funcionalidades más avanzadas. Para ello utilizaremos presentaciones visu
This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla's over 3 million students to learn about the future today!What is in the course?Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python!This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we've created this course to help guide students to learning a set of skills to make them extremely hirable in today's workplace environment.We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms.We cover advanced machine learning algorithms that most other courses don't! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN.This comprehensive course is designed to be on par with Bootcamps that usually cost thousands
Learn MLOps: Machine Learning Operations Complete Course
Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing?Are you interested in programming in Python, but you always afraid of coding?I think this course is for you!Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term.This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows:Chapter1: Introduction and all required installationsChapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib)Chapter3: PreprocessingChapter4: Machine Learning TypesChapter5: Supervised Learning: ClassificationChapter6: Supervised Learning: RegressionChapter7: Unsupervised Learning: ClusteringChapter8: Model TuningFurthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python.
As aplicações de Inteligência Artificial (IA) com Python têm desempenhado um papel significativo no setor financeiro, trazendo uma série de benefícios e transformando a forma como as instituições lidam com dados e tomam decisões. Aqui está um resumo da importância dessas aplicações em finanças:1. Tomada de Decisão Baseada em Dados: - A IA com Python capacita as instituições financeiras a tomar decisões mais informadas e precisas, utilizando algoritmos avançados para analisar grandes conjuntos de dados. Isso resulta em estratégias mais eficazes de investimento, gestão de riscos aprimorada e decisões mais fundamentadas.2. Previsão de Mercado e Tendências: - Algoritmos de machine learning e modelos de IA são utilizados para prever movimentos de mercado, identificar tendências e realizar análises preditivas. Isso auxilia investidores, traders e gestores de ativos na identificação de oportunidades e na mitigação de riscos.3. Detecção de Fraudes e Segurança: - Sistemas de IA são empregados para detectar padrões suspeitos e atividades fraudulentas em transações financeiras. Essa capacidade de análise em tempo real contribui para a segurança das transações e a proteção contra atividades fraudulentas.4. Gestão de Portfólio Automatizada: - Algoritmos de IA e aprendizado de máquina são usados para criar e otimizar automaticamente portfólios de investimento. Esses sistemas automatizados podem ajustar dinamicamente as alocações de ativos com base em condições de mercado em constante mudança.5. Atendimento ao Cliente e Chatbots: - A IA é aplicada em chatbots e assistentes virtuais para melhorar o atendimento ao cliente. Essas soluções são capazes de responder a consultas, fornecer informações sobre contas e até mesmo realizar transações simples, melhorando a eficiência e a experiência do cliente.6. Análise de Sentimento e Mí
Hello there,Welcome to the " Complete Data Science & Machine Learning A-Z with Python " CourseMachine Learning & Data Science all in one course with Python Data Visualization, Data Analysis Pandas & Numpy, KaggleMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data sciencePandas is an open source Python package that is most widely used for
This comprehensive lesson teaches how to test ML artifacts including code, data, and models to build a reliable ML system. It covers the intuition behind testing, different types of tests (unit, integration, system, acceptance, regression), best practices, and implementation details for testing code, data expectations, and model behavior.
Hello there,Welcome to the “Machine Learning & Data Science with Python & Kaggle | A-Z” course.Data Science & Machine Learning A-Z & Kaggle with Heart Attack Prediction projects and Machine Learning Python projectsMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data sciencePython instructors on OAK Academy specialize in everything from software d
Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. But, this course will give all the basics you need no matter for what objective you want to use it so if you :- Are a student and want to improve your programming skills and want to learn new utilities on how to use Python- Need to learn basics of Data science- Have to understand basic Data science tools to improve your career- Simply acquire the skills for personal useThen you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects.The structure of the courseThis course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools. Indeed, you will at first learn all the mathematics that are associated with Data science. This means that you will have a complete introduction to the majority of important statistical formulas and functions that exist. You will also learn how to set up and use Jupyter as well as Pycharm to write your Python code. After, you are going to learn different Python libraries that exist and how to use them properly. Here you will learn tools such as NumPy or SciPy and many others. Finally, you will have an introduction to machine learning and learn how a machine learning algorithm works. All this in just one course.Another very interesting thing about this course it contains a lot of practice. Indeed, I build all my course on a concept of learning by practice. In other words, this course contains a lot of practice this way you will be able to be sure that you completely underst
Hello there,Welcome to the “Complete Machine Learning & Data Science with Python | A-Z” course Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn, and dive into machine learning A-Z with Python and Data Science Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on OAK Academy here to help you apply machine learning to your work Complete machine learning & data science with python | a-z, machine learning a-z, Complete machine learning & data science with python, complete machine learning and data science with python a-z, machine learning using python, complete machine learning and data science, machine learning, complete machine learning, data scienceIt’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models Python, machine learning, django, python programming, machine learning python, python for beginners, data sciencePython instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small
Do you want to master NumPy and unlock your potential in data science? This course is your comprehensive, hands-on introduction to the foundational library of modern Python computing!NumPy is the absolute core building block for essential data science and machine learning libraries like Pandas, Scikit-learn, and PyTorch. By mastering it, you gain the technical edge needed for advanced topics like linear algebra, image processing, and fast numerical computations. If you want to start a career in Data Science or understand the engine behind Machine Learning in Python, this course is for you.What You'll Master in this Hands-On Python Course:This course will teach you everything you need to professionally use NumPy for scientific computing. We start with the basics and rapidly move into advanced techniques crucial for complex data science tasks.Foundation: Introduction to NumPy arrays, N-dimensional arrays, and the fundamental concepts of vectors and matrices.Data Analysis Tools: Leverage Universal Functions (ufuncs), Randomness, and Statistics to analyze and explore data efficiently in Python.Linear Algebra for ML: Master Basic and Advanced Linear Algebra operations, which are the backbone of all Machine Learning algorithms.Advanced Techniques: Understand Broadcasting and Advanced Indexing to write fast, memory-efficient Python code.Real-World Scientif
Hello there,Machine learning python, python, machine learning, django, ethical hacking, python bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, djangoWelcome to the “Machine Learning and Deep Learning A-Z: Hands-On Python ” course Python Machine Learning and Python Deep Algorithms in Python Code templates included Python in Data Science | 2021Do you know data science needs will create 11 5 million job openings by 2026?Do you know the average salary is $100 000 for data science careers!Deep learning a-z, machine learning a-z, deep learning, machine learning, machine learning & data science a-z: hands on python 2021, machine learning python, machine learning python, machine learning algorithms, python, Itsm, machine learning and deep learning a-z: hands on python, machine learning and deep learning a-z hands pn python, data science, rnn, deep learning python, data science a-z, recurrent neural network,Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on Udemy here to help you apply machine learning to your work Data Science Careers Are Shaping The FutureData science experts are needed in almost every field, from government security to dating apps Millions of businesses and government departments rely on big data to succeed and better serve their customers So data science careers are in high demandUdemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you<li
Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios. The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering. By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects. Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: About the Authors Selva Prabhakaran is a data scientist with a large e-commerce organization. In his 7 years of experience in data science, he has tackled comp
Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.We are going to execute following real-life projects,Kaggle Bike Demand Prediction from Kaggle competitionAutomation of the Loan Approval processThe famous IRIS ClassificationAdult Income Predictions from US Census DatasetBank Telemarketing PredictionsBreast Cancer PredictionsPredict Diabetes using Prima Indians Diabetes DatasetToday Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others. As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning? Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,Understanding of the overall landscape of Data Science and Machine LearningDifferent types of Data Analy
A comprehensive guide to understanding and implementing tree-based models and ensemble techniques in Python. The course covers Decision Trees, Random Forests, Bagging, AdaBoost, and XGBoost.
Learn Algorithmic Trading with Machine Learning
Industry-focused machine learning course with real-world projects and deployment strategies.
This course provides a comprehensive, hands-on introduction to machine learning on the Google Cloud Platform, with a specific focus on Vertex AI. Students will learn about various GCP services, including compute, storage, and databases, before diving into machine learning workflows. The curriculum covers building and deploying models using GCP's AutoML for tabular, image, and text data, as well as custom model training and deployment on the AI Platform and Vertex AI. The course is designed to equip learners with the practical skills needed to create and manage machine learning pipelines on Google Cloud.
Welcome to this comprehensive hands-on course on YOLOv10 for real-time object detection! YOLOv10 is the latest version in the YOLO family, building on the successes and lessons from previous versions to provide the best performance yet. This course is designed to take you from beginner to proficient in using YOLOv10 for various object detection tasks.Throughout the course, you will learn how to set up and use YOLOv10, label and create datasets, and train the model with custom data. The course is divided into three main parts:Part 1: Learning to Use YOLOv10 with Pre-trained Models In this section, we will start by setting up our environment using Google Colab, a free cloud-based platform with GPU support. You will learn to download and use pre-trained YOLOv10 models to detect objects in images. We will cover the following:Setting up the environment and installing necessary packages.Downloading pre-trained YOLOv10 models.Performing object detection on sample images.Visualizing and interpreting detection results.Part 2: Labeling and Making a Dataset with RoboFlowIn the second part, we will focus on creating and managing custom datasets using RoboFlow. This section will teach you how to:Create a project workspace on the RoboFlow website.Upload and annotate images accurately.Follow best practices for data labeling to ensure high-quality training results.Export labeled datasets in formats compatible with YOLOv10.Part 3: Training with Custom DatasetsThe final section of the course is dedicated to training YOLOv10 with your custom datasets. You will learn how to:Configure the training process, including setting parameters such as epochs and batch size.Train the YOLOv10 model using your labeled dataset from RoboFlow.Monitor training progress and evaluate the trained model.
The fifth course in the Google Advanced Data Analytics Certificate. You'll practice modeling variable relationships using methods such as linear regression, ANOVA, and logistic regression.
Are you planing to build your career in Data Science in This Year?Do you the the Average Salary of a Data Scientist is $100,000/yr?Do you know over 10 Million+ New Job will be created for the Data Science Filed in Just Next 3 years??If you are a Student / a Job Holder/ a Job Seeker then it is the Right time for you to go for Data Science!Do you Ever Wonder that Data Science is the "Most Hottest" Job Globally in 2018 - 2019!Above, we just give you a very few examples why you Should move into Data Science and Test the Hot Demanding Job Market Ever Created!The Good News is That From this Hands On Data Science and Machine Learning in R course You will Learn All the Knowledge what you need to be a MASTER in Data Science.Why Data Science is a MUST HAVE for Now A Days?The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let's have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science & Machine Learning!Here we list a Very Few Companies : -Google - For Advertise Serving, Advertise Targeting, Self Driving Car, Super Computer, Google Home etc. Google use Data Science + ML + AI to Take DecisionApple: Apple Use Data Science in different places like: Siri, Face Detection etcFacebook: Data Science , Machine Learning and AI used in Graph Algorithm for Find a Friend, Photo Tagging, Advertising Targeting, Chatbot, Face Detection etcNASA: Use Data Science For different PurposeMicrosoft: Amplifying human ingenuity with Data ScienceSo From the List of the Companies you can Understand all Big Giant to Very Small
Rigorous ML theory and implementation. Linear models, neural networks, deep learning, reinforcement learning.
This course teaches big ideas in machine learning like how to build and evaluate predictive models. This course provides an intro to clustering in R from a machine learning perspective.This online machine learning course is perfect for those who have a solid basis in R and statistics but are complete beginners with machine learning. You’ll get your first intro to machine learning.After learning the true fundamentals of machine learning, you'll experiment with the techniques that are explained in more detail. By the end, you'll be able to learn and build a decision tree and to classify unseen observations with k-Nearest Neighbors.Also, you'll be acquainted with simple linear regression, multi-linear regression, and k-Nearest Neighbors regression.This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more.At the end of this course, our machine learning and data science video tutorials, you’ll have a great understanding of all the main principles.Details of the course:Module 01: Basics of R toolIn this video, we are going to install r programming with rstudio in Windows Platform.Lab 01 R Installation and ConceptsIn this lab, we are going to learn about how we can install R Programing in Windows and learn about its several key concepts that are necessary for Programming in R.Video 2_R Programming ConceptsIn this video, we are going to learn the necessary concepts of RProgramming.Video 3_R Progrming ComputationsIn this tutorial, we will be learning about several mathematical algorithms and computations.Lab 02 R P
Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing?This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is "Learn by Doing"! For your satisfaction, I would like to mention few topics that we will be learning in this course:Basis Python programming for Data ScienceData Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and FilterNumPyArrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal FunctionsPandasPandas Data Structures - Series, DataF
Learn Imperial College LondonMathematics for Machine Learning: Linear AlgebraCourse
Il s'agit du cours en ligne le plus complet pour apprendre Python, la Data Science (science des données) et le Machine Learning (apprentissage automatique). Rejoignez-nous dès maintenant pour apprendre et maîtriser ces sujets !Que contient ce cours ?Bienvenue dans le cours le plus complet pour apprendre en ligne la Data Science et le Machine Learning ! Cette MasterClass a été conçue pour mettre en place ce qui semble être la meilleure façon de passer de zéro à héros pour la Data Science et le Machine Learning avec Python !Ce cours est conçu pour une personne qui connaît déjà un peu le langage Python et qui est prêt à s'immerger en profondeur dans l'utilisation de ces compétences Python pour la Data Science et le Machine Learning. Le salaire de départ typique d'un data scientist peut dépasser aisément les 100 000 euros annuel, et nous avons créé ce cours pour aider à guider les apprenants vers l'apprentissage d'un ensemble de compétences qui les rendront extrêmement intéressants (et attractifs !) dans le monde du travail actuel.Nous couvrirons tout ce que vous devez savoir sur la stack tech (compétences techniques) complète de Data Science et Machine Learning requise dans les meilleures entreprises du monde. Nos étudiants ont obtenu des emplois chez McKinsey, Facebook, Amazon, Google, Apple, Asana et d'autres grandes entreprises technologiques ! Nous avons structuré le cours en nous appuyant sur notre expérience de l'enseignement en ligne (et en présentiel) afin de proposer une approche claire et structurée. Cela vous guidera pour comprendre non seulement comment utiliser les bibliothèques populaires de Data Science et Machine Learning, mais aussi pourquoi et quand nous les utilisons. Ce cours est un équilibre parfait entre les études de cas pratiques issues du monde réel et la théorie mathématique qui se cache derrière les algorithmes de Machine Learning <strong
Hello there,Welcome to the “Python Programming: Machine Learning, Deep Learning | Python” coursePython, machine learning, python programming, django, ethical hacking, data analysis, python for beginners, machine learning python, python bootcampPython Machine Learning and Python Deep Learning with Data Analysis, Artificial Intelligence, OOP, and Python ProjectsComplete hands-on deep learning tutorial with Python Learn Machine Learning Python, go from zero to hero in Python 3Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small and the standard library is also large In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks Machine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work It’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathe
Complete Stanford CS229 Machine Learning course by Andrew Ng. Covers supervised learning, unsupervised learning, and best practices.
Practical machine learning tutorial series using Python. Covers regression, classification, clustering, neural networks with hands-on coding examples.
Python, Java, PyCharm, Android Studio and MNIST. Learn to code and build apps! Use machine learning models in hands-on projects. A wildly successful Kickstarter funded this courseExplore machine learning concepts. Learn how to use TensorFlow 1.4.1 to build, train, and test machine learning models. We explore Python 3.6.2 and Java 8 languages, and how to use PyCharm 2017.2.3 and Android Studio 3 to build apps. A machine learning framework for everyoneIf you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you.Be one of the firstThere are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.We provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and expand upon what I teach. Other courses don’t do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do. No prior knowledge is requiredWe will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners.Jump into a field that has more demand than supplyMachine learning changes everything. It’s bringing us self-driving cars, facial recognition and artificial intelligence. And the best part is: anyone can create such innovations."This course is GREA
Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platformLets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning MechanismThen we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on th
Learn Python for Data Science & Machine Learning from A-ZIn this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you!Python coding experience
Learn Feature Engineering for Machine Learning
An expert-level course covering advanced methods for causal discovery, effect estimation with high-dimensional data, and handling unobserved confounding.
Comprehensive Course Description:Electrification was undeniably one of the greatest engineering feats of the 20th century. The invention of the electric motor dates back to 1821, with mathematical analysis of electrical circuits following in 1827. However, it took several decades for the full electrification of factories, households, and railways to begin. Fast forward to today, and we are witnessing a similar trajectory with Artificial Intelligence (AI). Despite being formally founded in 1956, AI has only recently begun to revolutionize the way humanity lives and works.Similarly, Data Science is a vast and expanding field that encompasses data systems and processes aimed at organizing and deriving insights from data. One of the most important branches of AI, Machine Learning (ML), involves developing systems that can autonomously learn and improve from experience without human intervention. ML is at the forefront of AI, as it aims to endow machines with independent learning capabilities.Our "Data Science & Machine Learning Full Course in 90 Hours" offers an exhaustive exploration of both data science and machine learning, providing in-depth coverage of essential concepts in these fields. In today's world, organizations generate staggering amounts of data, and the ability to store, analyze, and derive meaningful insights from this data is invaluable. Data science plays a critical role here, focusing on data modeling, warehousing, and deriving practical outcomes from raw data.For data scientists, AI and ML are indispensable, as they not only help tackle large data sets but also enhance decision-making processes. The ability to transition between roles and apply these methodologies across different stages of a data science project makes them invaluable to any organization.What Makes This Course Unique?This course is designed to provide both theoretical foundations and practical, hands-on experience. By the end of the
Graduate-level ML course. Supervised learning, unsupervised learning, deep learning, reinforcement learning theory.
Learn AutoML: Automated Machine Learning
Embark on a comprehensive journey through the fascinating realm of data science and machine learning with our course, "Data Science and Machine Learning with Python and GPT 3.5." This course is meticulously designed to equip learners with the essential skills required to excel in the dynamic fields of data science and machine learning.Throughout this immersive learning experience, you will delve deep into the core concepts of data science and machine learning, leveraging the power of Python programming alongside the cutting-edge capabilities of ChatGPT 3.5. Our course empowers you to seamlessly navigate the entire data science workflow, from data acquisition and cleaning to exploratory data analysis and model deployment.You will master the art of cleaning raw data effectively, employing techniques tailored to handle missing values, diverse data types, and outliers, thus ensuring the integrity and quality of your datasets. Through hands-on exercises, you will become proficient in data manipulation using Python's pandas library, mastering essential techniques such as sorting, filtering, merging, and concatenating.Exploratory data analysis techniques will be thoroughly explored, empowering you to uncover valuable insights through frequencies, percentages, group-by operations, pivot tables, crosstabulation, and variable relationships. Additionally, you will gain practical experience in data preprocessing, honing your skills in feature engineering, selection, and scaling to optimize datasets for machine learning models.The course curriculum features a series of engaging projects designed to reinforce your understanding of key data science and machine learning concepts. You will develop expertise in building and evaluating supervised regression and classification models, utilizing a diverse array of algorithms including linear regression, random forest, decision tree, xgboost, logistic regression, KNN, lightgbm, and more.Unsupervised learning techniques will also b
Interested in the field of Machine Learning? Then this course is for you!Designed & Crafted by AI Solution Expert with 15 + years of relevant and hands on experience into Training , Coaching and Development.Complete Hands-on AI Model Development with Python. Course Contents are:Understand Machine Learning in depth and in simple process. Fundamentals of Machine LearningUnderstand the Deep Learning Neural Nets with Practical Examples.Understand Image Recognition and Auto Encoders.Machine learning project Life CycleSupervised & Unsupervised LearningData Pre-ProcessingAlgorithm SelectionData Sampling and Cross ValidationFeature EngineeringModel Training and ValidationK -Nearest Neighbor AlgorithmK- Means AlgorithmAccuracy DeterminationVisualization using SeabornYou will be trained to develop various algorithms for supervised & unsupervised methods such as KNN , K-Means , Random Forest, XGBoost model development. Understanding the fundamentals and core concepts of machine learning model building process with validation and accuracy metric calculation. Determining the optimum model and algorithm. Cross validation and sampling methods would be understood. Data processing concepts with practical guidance and code examples provided through the course. Feature Engineering as critical machine learning process would be explained in easy to understand and yet effective manner.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
Welcome to the Learn Data Science and Machine Learning with R from A-Z Course!In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job.The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R Programming to mastery.We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.Together we’re going to give you the foundational education that you need to know not just on how to write code in R, analyze and visualize data but also how to get paid for your newly developed programming skills.The cour
Unlock the power of Artificial Intelligence, Python, Machine Learning, Data Science, and Big Data Analytics in this comprehensive, hands-on course. Whether you’re a beginner or an aspiring data professional, this course equips you with the practical skills and knowledge to solve real-world problems using cutting-edge technologies.What You Will Learn:Fundamentals of Python programming for AI and data analysisBuilding and deploying Machine Learning models from scratchExploring Data Science techniques, including data cleaning, visualization, and analysisWorking with Big Data Analytics tools to handle massive datasetsImplementing AI solutions for real-world projects and business applicationsUnderstanding key concepts in Deep Learning, Neural Networks, and Predictive AnalyticsWho This Course is For:Anyone passionate about leveraging AI and Big Data to make smarter decisionsWhy Choose This Course:Hands-on projects and real-world examplesLearn from beginner-friendly to advanced concepts in a structured wayFocused on practical applications that can boost your career or businessCertificate after course completeBy the end of this course, you will have the confidence and skills to design and implement AI-powered solutions, build machine learning models, analyze complex datasets, and tackle big data challenges.Start your journey to becoming an AI, Machine Learning, and Data Science expert today!
Welcome to the comprehensive course on Predictive Analysis and Machine Learning Techniques! In this course, you will embark on a journey through various aspects of predictive analysis, from fundamental concepts to advanced machine learning algorithms. Whether you're a beginner or an experienced data scientist, this course is designed to provide you with the knowledge and skills needed to tackle real-world predictive modeling challenges.Through a combination of theoretical explanations, hands-on coding exercises, and practical examples, you will gain a deep understanding of predictive analysis techniques and their applications. By the end of this course, you'll be equipped with the tools to build predictive models, evaluate their performance, and extract meaningful insights from data.Join us as we explore the fascinating world of predictive analysis and unleash the power of data to make informed decisions and drive actionable insights!Section 1: Introduction This section serves as an introduction to predictive analysis, starting with an overview of Java Netbeans. Students will understand the basics of predictive modeling and explore algorithms like random forest and extremely random forest, laying the groundwork for more advanced topics in subsequent sections.Section 2: Class Imbalance and Grid Search Here, students delve into more specialized topics within predictive analysis. They learn techniques for addressing class imbalance in datasets, a common challenge in machine learning. Additionally, they explore grid search, a method for systematically tuning hyperparameters to optimize model performance.Section 3: Adaboost Regressor The focus shifts to regression analysis with the Adaboost algorithm. Students understand how Adaboost works and apply it to predict traffic patterns, gaining practical experience in regression modeling.Section 4: Detecting Patterns with Unsupervised Learning</strong
Programmer en Python pour la Data Science, le Machine Learning, la DataViz et l'Intelligence ArtificielleCe cours a pour objectif de vous initier à la programmation en Python en lien avec les concepts essentiels du Big Data (Data Science, Machine Learning, IA, etc.). Il ne requiert aucun prérequis et vous permet d'atteindre un niveau solide en seulement 4 heures de formation.Acquérir des bases solidesPlus besoin de partir à la chasse aux informations sur Google, l'essentiel de votre apprentissage est concentré dans ce cours.Gagner du tempsCe cours est conçu pour vous familiariser avec la Data Science et Python de manière rapide et efficace. Vous pourrez ainsi atteindre un niveau solide en seulement 4 heures de cours.Une formation qui va à votre rythmeLes concepts sont présentés progressivement, à travers des exemples concrets issus de projets d'entreprises et d'universités, vous permettant d'appliquer ce que vous avez appris.Cours récent et régulièrement mis à jourMis à jour récemment, ce cours est en adéquation avec les compétences actuellement recherchées par les entreprises.Éviter les pièges de débutantsCe cours détaille les bonnes pratiques d'un Data Scientist expérimenté pour rédiger un code de qualité professionnelle.Préparation réussie pour vos examens, certifications et tests techniques sur PythonLes exercices inclus dans ce cours constituent un excellent moyen de préparation pour vos examens, certifications et tests techniques en entreprise.Travailler pour les plus grandes entreprisesDes entreprises prestigieuses telles qu'Intel, Google, Netflix, Spotify, Meta, mais aussi Renault, la SNCF, Orange, Total, Capgemini, sont actuellement à la recherche de Data Scientists expérimentés maîtrisant Python.Se former à des métiers actuellement recherchés</stron
This course covers the theory behind support vector machines and how to implement and optimize a Support Vector Classifier in Python using sk-learn.
Learn Machine Learning for Business Analytics
Formation Complète Data Science et Machine Learning avec PythonDevenez Data Scientist et Maîtrisez l’Apprentissage Automatique avec PythonÊtes-vous prêt à acquérir les compétences les plus recherchées dans la tech et l’analyse de données ? Cette formation complète en Data Science et Machine Learning avec Python vous guidera pas à pas, même si vous partez de zéro, pour devenir un expert capable de transformer des données en décisions stratégiques.Pourquoi choisir cette formation ?Le métier de Data Scientist figure parmi les plus demandés et les mieux rémunérés. Grâce à cette formation unique, vous apprendrez à :Analyser et manipuler des données complexes avec Python.Créer des visualisations impactantes et interactives.Développer et entraîner des modèles prédictifs avancés.Maîtriser les principales bibliothèques Python en Data Science.Un programme complet et progressifAvec plus de 100 vidéos HD, des notebooks Jupyter détaillés, des exemples concrets et des exercices pratiques, vous progresserez étape par étape jusqu’à devenir autonome.Voici un aperçu de ce que vous allez maîtriser :Programmation et traitement des donnéesProgrammation avec Python orienté Data ScienceManipulation des tableaux numériques avec NumPyGestion et analyse de données tabulaires avec PandasLecture et traitement des fichiers CSV et ExcelVisualisation de donnéesCréation de graphiques professionnels avec MatplotlibAnalyse exploratoire et visualisations avancées avec SeabornMachine Learning supervisé et non supervisé avec Scikit-Lear
Jetzt neu: Zusätzlicher Bonus zum Thema Deep Learning (Neuronale Netze) mit Python, Tensorflow und Keras!Dieser Kurs enthält über 300 Lektionen, Quizze, Praxisbeispiele, ... - der einfachste Weg, wenn du Machine Learning lernen möchtest. Schritt für Schritt bringe ich dir maschinelles Lernen bei. In jedem Abschnitt lernst du ein neues Thema - zuerst die Idee / Intuition dahinter, und anschließend den Code sowohl in Python als auch in R.Machine Learning macht erst dann richtig Spaß, wenn man echte Daten auswertet. Deswegen analysierst du in diesem Kurs besonders viele Praxisbeispiele:Schätze den Wert von GebrauchtwagenSchreibe einen Spam-FilterDiagnostiziere BrustkrebsSchreibe ein Programm, was die Bedeutung von Adjektiven lerntLese Zahlen aus Bildern einAlle Codebeispiele werden dir beiden Programmiersprachen gezeigt - du kannst also wählen, ob du den Kurs in Python, R, oder in beiden Sprachen sehen möchtest!Nach dem Kurs kannst du Machine Learning auch auf eigene Daten anwenden und eigenständig fundierte Entscheidungen treffen:Du weißt, wann welche Modelle in Frage kommen könnten und wie du diese vergleichst. Du kannst analysieren, welche Spalten benötigt werden, ob zusätzliche Daten benötigt werden, und weißt, die die Daten vorab aufbereitet werden müssen. Dieser Kurs behandelt alle wichtigen Themen:RegressionKlassifizierungClusteringNatural Language ProcessingBonus: Deep Learning (nur für Python, weil die Tools hier sehr viel ausgereifter sind)Zu allen diesen Themen lernst du verschiedene Algorithmen kennen. Die Ideen dahinter werden einfach erklärt - keine trockenen, mathematischen Formeln, sondern anschauliche, grafische Erklärungen.Wir verwenden hierbei g
Comprehensive ML course covering regression, classification, clustering, deep learning, NLP, reinforcement learning.
Selamat datang di program pelatihan data science dan machine learning dengan Python!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan Python.Bagi rekan - rekan yang belum menguasai pemrograman Python, pelatihan juga memberikan konten pemrograman dasar untuk Python sehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman Python, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihanPemrograman PythonPython Virtual EnvironmentPengolahan dan Analisa Data - Numpy dan PandasTopik Khusus - Numpy dan Pandas - DatabaseVisualisasi Data dengan memanfaatkan library Matplotlib, Seaborn dan BokehTopik Khusus Visualisasi Data Time SeriesDataset, Pra-Proses dan Pengurangan Dimensi Feature (Dimensionality Reduction)Permasalahan dan Penyelesaian Kasus Linear RegressionPermasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Hyperparameter Tuning Untuk Model Machine LearningEnsemble MethodsReinforcement LearningAutomated Machine Learning (AutoML)Kumpulan Studi KasusJika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan
You want to be able to perform your own data analyses with R? You want to learn how to get business-critical insights out of your data? Or you want to get a job in this amazing field? In all of these cases, you found the right course!We will start with the very Basics of R, like data types and -structures, programming of loops and functions, data im- and export.Then we will dive deeper into data analysis: we will learn how to manipulate data by filtering, aggregating results, reshaping data, set operations, and joining datasets. We will discover different visualisation techniques for presenting complex data. Furthermore find out to present interactive timeseries data, or interactive geospatial data.Advanced data manipulation techniques are covered, e.g. outlier detection, missing data handling, and regular expressions.We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ...You will also learn to develop web applications and how to deploy them with R/Shiny.For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.You will understand the advantages and disadvantages of d
3.997 / 5.000Aprender a programar en Python no siempre es fácil, especialmente si desea usarlo para la ciencia de datos. De hecho, hay muchas herramientas diferentes que deben aprenderse para poder usar correctamente Python para la ciencia de datos y el aprendizaje automático, y cada una de esas herramientas no siempre es fácil de aprender. Pero, este curso le dará todos los conceptos básicos que necesita sin importar para qué objetivo quiera usarlo, así que si: - Es estudiante y desea mejorar sus habilidades de programación y desea aprender nuevas utilidades sobre cómo usar Python - Necesidad de aprender los conceptos básicos de la ciencia de datos. - Debe comprender las herramientas básicas de ciencia de datos para mejorar su carrera. - Simplemente adquiera las habilidades para uso personal Entonces definitivamente te encantará este curso. No solo aprenderá todas las herramientas que se utilizan para la ciencia de datos, sino que también mejorará su conocimiento de Python y aprenderá a usar esas herramientas para poder visualizar sus proyectos. La estructura del curso Este curso está estructurado de manera que podrá aprender cada herramienta por separado y practicar programando en Python directamente con el uso de esas herramientas. De hecho, al principio aprenderá todas las matemáticas asociadas con la ciencia de datos. Esto significa que tendrá una introducción completa a la mayoría de las funciones y fórmulas estadísticas importantes que existen. También aprenderá a configurar y utilizar Jupyter, así como a escribir su código Python. Después, aprenderá las diferentes bibliotecas de Python que existen y cómo usarlas correctamente. Aquí aprenderás herramientas como NumPy o muchas otras.Finalmente, tendrá una introducción al aprendizaje automático y aprenderá cómo funciona un
Machine learning has become one of the most common practices used by many organizations, groups and individuals. It helps various software to predict the outcome more precisely without any programming. Machine learning finds the pattern in the input data and uses statistical analysis to foretell the result. To support its extensive requirements, Tensorflow was launched by Google. In order to provide next-generation machine learning solutions, we have hand-picked this course covering all its aspects. Why this course is important? Machine learning often requires heavy computation and for that Tensorflow was developed as an open source library. Tensorflow not only does the heavy computation but can also build dataflows. Apart from machine learning, it is also used in wide variety of other domains by the experts. This course contains different topics to make you understand everything about next-generation machine learning by Tensorflow. What makes this course so valuable? It includes all the basics of Tensorflow with detail description of tensors, operators and variables. Installation of Tensorflow on Windows, Mac and Linux is clearly shown. Additionally, it gives insights into the basics of machine learning and its types. This course also covers various algorithms like linear regression, logistic regression, NN regression, K-Means algorithm and others. Herein, advanced machine learning is also well elaborated with the topics of neural networks, convolution neural networks, recurrent neural networks and so on. This course includes- 1.Tensorflow fundamentals and installation 2. Details about tensors, operators, variables and others 3. Details about machine learning, inference and its types 4. Different algorithms like linear regression, logistic regression, clustering, K-means algorithm, kernels and many more 5. Various advanced learning networks and its implementation - Neural Networks, Conv
Data Science is an interdisciplinary field that leverages statistical analysis, data exploration, and machine learning techniques to derive knowledge and meaningful insights from data.Definition of Data Science:Data Science encompasses various processes, including data acquisition, thorough analysis, and informed decision-making.Data Science involves the identification and interpretation of data patterns to make predictive assessments.Through the application of Data Science, organizations can achieve:1. Improved decision-making processes, enabling the selection between alternatives (A or B) with greater confidence.2. Predictive analysis that anticipates future events or trends, aiding in proactive planning.3. Discovery of hidden patterns and valuable information within datasets, leading to actionable insights.Applications of Data Science:Data Science finds extensive application across diverse industries such as banking, consultancy, healthcare, and manufacturing.Examples of Data Science applications include:1. Optimizing route planning for shipping purposes.2. Anticipating potential delays in flights, ships, trains, etc., through predictive analysis.3. Crafting personalized promotional offers for customers.4. Determining the best time to deliver goods for maximum efficiency.5. Forecasting future revenue for a company.6. Analyzing the health benefits of specific training regimens.7. Predicting election outcomes.Data Science Integration in Business:Data Science can be seamlessly integrated into various facets of business operations where relevant data is available, including:1. Consumer goods industries for market analysis and consumer behavior prediction.2. Stock markets for financial analysis and forecasting.3. Industrial settings for process optimization and quality control.4. Political scenarios for opinion
In the dynamic and rapidly evolving landscape of data science and machine learning, certification serves as a powerful testament to your expertise and a crucial stepping stone in your career progression. The "Data Science & Machine Learning Proficiency Exam March 2025" represents a significant milestone for intermediate professionals seeking to validate their skills and solidify their position within the industry. This course is meticulously designed to provide you with the comprehensive knowledge, practical experience, and strategic insights necessary to not only pass this exam but to excel in the real-world applications of data science and machine learning.Why This Course?This course goes beyond simple memorization and rote learning. It’s a journey of deep understanding, practical application, and strategic exam preparation. We recognize that intermediate learners possess a foundational knowledge base but require targeted guidance to refine their skills and bridge the gap between theoretical understanding and practical proficiency. Therefore, this course is designed to:Provide a Structured Learning Path: The curriculum is structured to follow the exam's blueprint, ensuring that you cover all essential topics in a logical and progressive manner.Offer Real-World Relevance: We emphasize the practical application of concepts, demonstrating how data science and machine learning are used to solve real-world problems.Deliver Targeted Practice: Realistic practice exams and quizzes are designed to simulate the actual exam experience, allowing you to build confidence and identify areas for improvement.Foster Deep Understanding: In-depth explanations and detailed examples help you grasp complex concepts and develop a strong foundation in data science and machine learning.Ensure March 2025 Readiness: The course content is co
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Готовы ли вы начать свой путь, чтобы стать Data Scientist?Специалист по анализу данных - одна из наиболее подходящих профессий для процветания в этом веке. Он цифровой, ориентированный на программирование и аналитический. Поэтому неудивительно, что спрос на специалистов по анализу данных на рынке труда растет.Однако предложение было очень ограниченным. Трудно получить навыки, необходимые для работы в качестве специалиста по данным.И как это сделать?Университеты не спешили создавать специализированные программы по науке о данных. (Не говоря уже о том, что существующие очень дороги и требуют много времени)Большинство онлайн-курсов сосредоточено на конкретной теме, и трудно понять, как навыки, которым они обучают, вписываются в общую картину.Этот всеобъемлющий курс станет вашим руководством к изучению того, как использовать возможности Python для анализа данных, создания красивых визуализаций и использования мощных алгоритмов машинного обучения! Курс регулярно пополняется новыми материалами!Этот курс подойдёт для всех - для начинающих без опыта программирования, для имеющих некоторый опыт программирования и для опытных разработчиков, стремящихся изучить Data Science!Вы научитесь программировать на Python, создавать удивительные визуализации данных и использовать машинное обучение с Python! Чему вы научитесь:Применять Python для Data ScienceИспользовать инструменты для работы в Data Science Научитесь использовать NumPy для числовых данныхНаучитесь использовать Pandas для анализа данныхНаучитесь использовать Matplotlib для визуализации данныхНаучитесь использовать Seaborn для визуализации данныхНаучитесь использовать встроенную визуализацию библиотеки PandasНаучитесь применять новые знания на практикеНаучитесь использовать библиотеки Machine LearningИ многое другое!Записывайтесь на курс и получите одну из самых востребованных профессий и супер
An interactive, hands-on course where you learn to build and use decision trees and random forests, two powerful supervised machine learning models. Includes projects to solidify your understanding.
Learn All Machine Learning algorithms explained in 17 min
This certificate program from Cornell University covers the fundamentals of machine learning. It includes a specific course on 'Debugging and Improving Machine Learning Models' where you will learn to identify causes of prediction error, understand the bias-variance trade-off, and use ensemble methods to improve model performance.
A specific course within Cornell's Machine Learning Certificate program that focuses on investigating the prediction accuracy of machine learning algorithms. You will learn to recognize high bias and variance to reduce prediction errors and implement techniques like bagging and boosting to create more reliable models.
Learn How To Learn Math for Machine Learning FAST (Even With Zero Math Background)
Unlock the potential of data-driven insights with our comprehensive course, "Deep Dive into Mastering Data Science and Machine Learning." In today's data-driven world, the ability to extract knowledge, predict trends, and make informed decisions is a crucial skill. This course is designed to empower you with the expertise required to navigate the intricate landscape of data science and machine learning.**Course Highlights:**Dive into Data: Learn to wrangle, clean, and preprocess data from various sources, preparing it for in-depth analysis. Discover techniques to identify and handle missing values, outliers, and anomalies that could affect your analysis.Algorithm Mastery: Delve into the world of machine learning algorithms, from foundational concepts to cutting-edge techniques. Understand the nuances of classification, regression, clustering, and recommendation systems, and explore ensemble methods and deep learning architectures for enhanced performance.Visualize Insights: Develop the art of data visualization to effectively communicate your findings. Learn to create compelling graphs, plots, and interactive dashboards that bring data to life and aid decision-making.Real-world Projects: Put theory into practice with hands-on projects that simulate real-world scenarios. Tackle challenges ranging from predicting customer behavior to image recognition, gaining experience that mirrors the complexities of the field.Ethical and Transparent AI: Understand the ethical considerations in data science and machine learning. Explore methods to interpret and explain model predictions, ensuring transparency and accountability in your applications.Model Deployment: Take your models from the development stage to real-world deployment. Learn about containerization, cloud services, and deployment pipelines, ensuring your solutions are accessible and scalable.Peer Learning: Engage with a
Clear and simple explanations of machine learning algorithms. Understand the math and intuition behind ML with Josh Starmer.
This course covers fundamental mathematical concepts for machine learning, including derivatives, integrals, and optimization techniques like gradient descent.
Learn How I'd learn ML in 2025 (if I could start over)
A focused course on logistic regression and other supervised machine learning techniques using Python.
Course materials for a graduate-level class on optimization for machine learning, including lecture notes and problem sets.
Interested in Machine Learning and Deep Learning ? Then this course is for you!This course is about the fundamental concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking.In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow.### MACHINE LEARNING ###Linear Regressionunderstanding linear regression modelcorrelation and covariance matrixlinear relationships between random variablesgradient descent and design matrix approachesLogistic Regressionunderstanding logistic regressionclassification algorithms basicsmaximum likelihood function and estimationK-Nearest Neighbors Classifierwhat is k-nearest neighbour classifier?non-parametric machine learning algorithmsNaive Bayes Algorithmwhat is the naive Bayes algorithm?classification based on probabilitycross-validation overfitting and underfittingSupport Vector Machines (SVMs)support vector machines (SVMs) and support vector classifiers (SVCs)maximum margin classifierkernel trickDecision Trees and Random Forestsdecision tree classifierrandom forest classifiercombining weak learnersBagging and
The Python for Data Science and Machine Learning course is designed to equip learners with a comprehensive understanding of Python programming, data science techniques, and machine learning algorithms. Whether you are a beginner looking to enter the field or a seasoned professional seeking to expand your skillset, this course provides the knowledge and practical experience necessary to excel in the rapidly growing field of data science.Course Objectives:1. Master Python Programming: Develop a strong foundation in Python programming, including syntax, data structures, control flow, and functions. Gain proficiency in using Python libraries such as NumPy, Pandas, and Matplotlib to manipulate and visualize data effectively.2. Data Cleaning and Preprocessing: Learn how to handle missing data, outliers, and inconsistent data formats. Acquire skills in data cleaning and preprocessing techniques to ensure the quality and reliability of datasets.3. Exploratory Data Analysis: Understand the principles and techniques of exploratory data analysis. Learn how to extract insights, discover patterns, and visualize data using statistical methods and Python libraries.4. Statistical Analysis: Gain a solid understanding of statistical concepts and techniques. Apply statistical methods to analyze data, test hypotheses, and draw meaningful conclusions.5. Machine Learning Fundamentals: Learn the foundations of machine learning, including supervised and unsupervised learning, regression, classification, and clustering. Understand the strengths and limitations of different machine learning algorithms.6. Machine Learning Implementation: Gain hands-on experience in implementing machine learning models using Python libraries such as scikit-learn. Learn how to train, evaluate, and optimize machine learning models.7. Feature Engineering and Selection: Develop skills in feature engineering to create mea
This course covers the entire process of building, evaluating, and operationalizing machine learning models. You will learn to assess model performance using key metrics and cross-validation techniques and explore methods for improving model efficiency.
A course that focuses specifically on classification techniques within supervised learning, offered by IBM.
Learn Precision Agriculture with AI and Machine Learning
Welcome to the ultimate ChatGPT and Python Data Science course—your golden ticket to mastering the art of data science intertwined with the latest AI technology from OpenAI.This course isn't just a learning journey—it's a transformative experience designed to elevate your skills and empower you with practical knowledge.With AI's recent evolution, many tasks can be accelerated using models like ChatGPT. We want to share how to leverage AI it for data science tasks.Embark on a journey that transcends traditional learning paths. Our curriculum is designed to challenge and inspire you through:Comprehensive Challenges: Tackle 10 concrete data science challenges, culminating in a case study that leverages our unique 365 data to address genuine machine learning problems.Real-World Applications: From preprocessing with ChatGPT to dissecting a furniture retailer's client database, explore a variety of industries and data types.Advanced Topics: Delve into retail data analysis, utilize regular expressions for comic book analysis, and develop a ChatGPT-powered movie recommendation system. Engage with such critical topics as AI ethics to combat biases and ensure data privacy.This course emphasizes practical application over theoretical knowledge, where you will:Perform dynamic sentiment analysis using a Naïve Bayes algorithm.Craft nuanced classification reports with our proprietary data.Gain hands-on experience with real datasets—preparing you to solve complex data science problems confidently.We’ll be using ChatGPT, Python, and Jupyter Notebook throughout the course, and I’ll link all the datasets, Notebooks for you to play around with on your own.I'll help you create a ChatGPT profile, but I’ll assume you're adept in Python and somewhat experienced in machine learning. Are you ready to dive into the
Welcome to "Machine Learning: Modern Computer Vision & Generative AI," a cutting-edge course that explores the exciting realms of computer vision and generative artificial intelligence using the KerasCV library in Python. This course is designed for aspiring machine learning practitioners who wish to explore the fusion of image analysis and generative modeling in a streamlined and efficient manner.Course Highlights:KerasCV Library: We start by harnessing the power of the KerasCV library, which seamlessly integrates with popular deep learning backends like Tensorflow, PyTorch, and JAX. KerasCV simplifies the process of writing deep learning code, making it accessible and user-friendly.Image Classification: Gain proficiency in image classification techniques. Learn how to leverage pre-trained models with just one line of code, and discover the art of fine-tuning these models to suit your specific datasets and applications.Object Detection: Dive into the fascinating world of object detection. Master the art of using pre-trained models for object detection tasks with minimal effort. Moreover, explore the process of fine-tuning these models and learn how to create custom object detection datasets using the LabelImg GUI program.Generative AI with Stable Diffusion: Unleash the creative potential of generative artificial intelligence with Stable Diffusion, a powerful text-to-image model developed by Stability AI. Explore its capabilities in generating images from textual prompts and understand the advantages of KerasCV's implementation, such as XLA compilation and mixed precision support, which push the boundaries of generation speed and quality.Course Objectives:Develop a strong foundation in modern computer vision techniques, including image classification and object detection.Acquire hands-on experience in using pre-t
This course explores supervised learning techniques for marketing applications. The curriculum covers customer behavior analysis, product recommendation systems, and customer lifetime value prediction.
If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.Hand-on examples are available for you to download.Please watch the first two videos to have a better understanding of the course.TOPICS COVEREDWhat is Machine Learning?Linear RegressionSteps to Calculate the ParametersLinear Regression-Gradient Descent using Mean Squared Error (MSE) Cost FunctionLogistic Regression: ClassificationDecision BoundarySigmoid FunctionNon-Linear Decision BoundaryLogistic Regression: Gradient DescentGradient Descent using Mean Squared Error Cost FunctionProblems with MSE Cost Function for Logistic RegressionIn Search for an Alternative Cost-FunctionEntropy and Cross-EntropyCross-Entropy: Cost Function for Logistic RegressionGradient Descent with Cross Entropy Cost FunctionLogistic Regression: Multiclass ClassificationIntroduction to Neural NetworkLogical OperatorsModeling Logical Operators using Perceptron(s)Logical Operators using Combination of Perceptron
This course covers advanced machine learning topics, including a detailed section on ensemble learning with decision trees, random forests, and gradient boosting.
Welcome to the first Data Science and Machine Learning course with ChatGPT. Learn how to use ChatGPT to master complex Data Science and Machine Learning real-life projects in no time! Why is this a game-changing course?Real-world Data Science and Machine Learning projects require a solid background in advanced statistics and Data Analytics. And it would be best if you were a proficient Python Coder. Do you want to learn how to master complex Data Science projects without the need to study and master all the required basics (which takes dozens if not hundreds of hours)? Then this is the perfect course for you! What you can do at the end of the course:At the end of this course, you will know and understand all strategies and techniques to master complex Data Science and Machine Learning projects with the help of ChatGPT! And you don´t have to be a Data Science or Python Coding expert! Use ChatGPT as your assistant and let ChatGPT do the hard work for you! Use ChatGPT forthe theoretical part Python codingevaluating and interpreting coding and ML resultsThis course teaches prompting strategies and techniques and provides dozens of ChatGPT sample prompts toload, initially inspect, and understand unknown datasets clean and process raw datasets with Pandasmanipulate, aggregate, and visualize datasets with Pandas and matplotlibperform an extensive Explanatory Data Analysis (EDA) for complex datasetsuse advanced statistics, multiple regression analysis, and hypothesis testing to gain further insightsselect the most suitable Machine Learning Model for your prediction tasks (Model Selection)evaluate and interpret the performance of your Machine Learning models (Perfo
This course delves into more advanced regression topics, including generalized linear models, mixed-effects models, and survival analysis, using the R programming language.
Learn DeepLearning.AIUnsupervised Learning, Recommenders, Reinforcement LearningCourse
A 7-day crash course designed to get developers started with function optimization in Python. It covers topics like grid search, SciPy optimization algorithms, BFGS, hill-climbing, simulated annealing, and gradient descent.
AI is omnipresent in our modern world. It is in your phone, in your laptop, in your car, in your fridge and other devices you would not dare to think of. After thousands of years of evolution, humanity has managed to create machines that can conduct specific intelligent tasks when trained properly. How? Through a process called machine learning or deep learning, by mimicking the behaviour of biological neurons through electronics and computer science. Even more than it is our present, it is our future, the key to unlocking exponential technological development and leading our societies through wonderful advancements. As amazing as it sounds, it is not off limits to you, to the contrary!We are both engineers, currently designing and marketing advanced ultra light electric vehicles. Albert is a Mechanical engineer specializing in advanced robotics and Eliott is an Aerospace Engineer specializing in advanced space systems with past projects completed in partnership with the European Space Agency. The aim of this course is to teach you how to fully, and intuitively understand neural networks, from their very fundamentals. We will start from their biological inspiration through their mathematics to go all the way to creating, training and testing your own neural network on the famous MNIST database.It is important to note that this course aims at giving you a complete and rich understanding of neural networks and AI, in order to give you the tools to create your own neural networks, whatever the project or application. We do this by taking you through the theory to then apply it on a very hands-on MATLAB project, the goal being for you to beat our own neural network's performance!This course will give you the opportunity to understand, use and create:How to emulate real brains with neural networks.How to represent and annotate neural networks.How to build and compute neural ne
A comprehensive course on machine learning from NPTEL that includes modules on Support Vector Machines and Kernel Methods. The course is available for free online.
This course will help you in understanding of the Linear Algebra and math’s behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.There’re tons of topics in this course. To begin the course:We have a discussion on what is Linear Algebra and Why we need Linear AlgebraThen we move on to Getting Started with R, where you will learn all about how to setup the R environment, so that it’s easy for you to have a hands-on experience.Then we get to the essence of this course;Vectors & Operations on VectorsMatrices & Operations on MatricesDeterminant and InverseSolving Systems of Linear EquationsNorms & Basis VectorsLinear IndependenceMatrix FactorizationOrthogonalityEigenvalues and EigenvectorsSingular Value Decomposition (SVD)Again, in each of these sections you will find R code demos and solved problems apart from the theoretical concepts of Linear Algebra.You will also learn how to use the R's pracma, matrixcalc library which contains numerous functions for matrix computations and solving Linear Algebric problems. So, let’s get started….
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A comprehensive program designed to equip professionals with the skills to harness AI and machine learning for strategic decision-making. The course covers AI-driven decision-making strategies, data-driven decision-making with AI, and the application of predictive analytics in real-world business scenarios.
Unlock the boundless potential of data by enrolling in our comprehensive course, "Mastering Machine Learning, Data Science, Neural Networks, and Artificial Intelligence with Python and Libraries." This meticulously crafted program is designed to empower individuals with the skills and knowledge needed to navigate the dynamic landscape of modern technology.Course Overview:In this immersive learning journey, participants will delve into the core principles of Machine Learning, Data Science, Neural Networks, and Artificial Intelligence using Python as the primary programming language. The course is structured to cater to both beginners and intermediate learners, ensuring a gradual progression from fundamental concepts to advanced applications.Key Highlights:Foundations of Machine Learning:Gain a solid understanding of machine learning fundamentals, algorithms, and models.Explore supervised and unsupervised learning techniques.Master feature engineering, model evaluation, and hyperparameter tuning.Data Science Essentials:Learn the art of extracting valuable insights from data.Acquire proficiency in data manipulation, cleaning, and exploratory data analysis.Harness the power of statistical analysis for informed decision-making.Neural Networks and Deep Learning:Dive into the realm of neural networks and deep learning architectures.Understand the mechanics of artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).Implement state-of-the-art deep learning models using Python libraries.Artificial Intelligence (AI) Applications:Explore the practical applications of AI in various industries.Wor
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently a
This course in the DeepLearning.AI specialization covers the essential calculus concepts needed to understand and build machine learning models.
This course covers a comprehensive curriculum from the basics of Python and math to advanced topics in Machine Learning, Deep Learning, and LLMs, including Transformers. It appears to be a broad AI course that likely covers the hardware aspects of training and deploying models.
Learn Artificial Intelligence Foundations: Machine Learning
A collection of talks from a workshop on optimization for large-scale machine learning, featuring leading researchers in the field.
Learn Machine Learning Engineering for Production (MLOps)
Equips professionals with skills to optimize energy systems using AI. The curriculum focuses on predictive analytics, energy forecasting, and smart grid optimization through hands-on projects.
An undergraduate certificate program that blends AI-driven fashion insights with machine learning techniques to revolutionize styling trends and personalization. The course teaches how to analyze fashion data, predict trends, and create personalized styling solutions. It is designed for students, designers, and marketers looking to gain cutting-edge skills in the digital fashion industry.
Learn IBM Watson Machine Learning Professional Certificate
4.13/5.0 rating. 100% say "valuable information." 100% say "clear explanations." 100% say "knowledgeable instructor." Beginner-friendly introduction to artificial intelligence fundamentals, machine learning, and real-world AI applications.Master Artificial Intelligence basics including machine learning concepts, neural networks, deep learning principles, and AI applications across industries. Learn AI fundamentals without coding—designed for engineers, business professionals, and beginners exploring how intelligent systems transform healthcare, finance, manufacturing, energy, and project management.WHAT YOU'LL LEARNAI Fundamentals & TypesUnderstand what artificial intelligence is, differentiate between narrow AI, general AI, and super AI, and learn how AI systems learn from data. Explore supervised learning, unsupervised learning, and reinforcement learning concepts without complex mathematics.Machine Learning BasicsMaster foundational machine learning concepts including training data, algorithms, model accuracy, and prediction systems. Learn how ML powers recommendation engines, fraud detection, and predictive maintenance without writing code.Neural Networks & Deep LearningUnderstand how artificial neural networks mimic human brain structure, learn about layers, nodes, activation functions, and how deep learning enables image recognition, natural language processing, and autonomous systems.Data in AI SystemsLearn why data is critical for AI, understand training datasets, data quality requirements, data preprocessing, and how bias in data creates biased AI models. Explore data ethics and responsible AI practices.AI Applications Across IndustriesDiscover real-world AI uses in energy systems (predictive maintenance, grid optimization), manufacturing (quality control, robotics), healthcare (diagnostics, drug disco
This course delves into natural language processing (NLP), teaching you how to build models for tasks like sentiment analysis and text classification. You'll learn about logistic regression and naive Bayes, and how to represent text as vectors.
Learn State-of-the-Art Machine Learning Papers Implementation
Scopri l'avanguardia del linguaggio e della tecnologia con il mio Percorso Formativo Esclusivo su ChatGPT e Machine Learning!Siamo sulla soglia di una nuova era in cui l'intelligenza artificiale e il machine learning stanno ridefinendo il modo in cui interagiamo con la tecnologia. Per esempio, potrai usare ChatGPT per automatizzare la risposta alle domande dei tuoi clienti o per creare contenuti di blog in modo automatico. Sei pronto a essere al centro di questa rivoluzione? Allora, il mio corso è l'investimento perfetto per il tuo futuro!Questo corso è stato progettato per fornire una comprensione approfondita dei concetti chiave di Machine Learning e delle tecniche di Prompt Engineering, utilizzando come base le versioni disponibili al momento della creazione del corso.Sebbene la piattaforma possa subire aggiornamenti o modifiche nel tempo, i concetti e le strategie insegnati in questo corso sono progettati per essere universalmente applicabili, anche alle versioni più recenti e a quelle future. Questo ti permetterà di acquisire competenze durature e facilmente adattabili al progresso delle tecnologie IA.Immergiti nel cuore del machine learning, delle reti neurali e dei modelli di linguaggio con questo percorso formativo. Scoprirai ChatGPT, un software che utilizza uno dei modelli di linguaggio più avanzati al mondo, e imparerai a utilizzarlo per creare contenuti coinvolgenti, ottimizzare le tue tecniche di marketing e portare il tuo lavoro o i tuoi studi al livello successivo.Con il nostro corso, avrai l'opportunità di esplorare: I fondamenti del Machine Learning e le diverse tipologie di apprendimento; La struttura e il funzionamento delle reti neurali; L'architettura e l'apprendimento di ChatGPT; Le tecniche di Prompt Engineering per generare contenuti coinvolgenti; L'importan
Learn AI, Machine Learning, Deep Learning and Generative AI Explained
A tutorial on optimization algorithms for machine learning, focusing on gradient-based and stochastic methods.
Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.This Data Science & Machine Learning (ML) course is not only ‘Hands-On’ practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI. In this course several Machine Learning (ML) projects are included.1) Project - Customer Segmentation Using K Means Clustering2) Project - Fake News Detection using Machine Learning (Python)3) Project COVID-19: Coronavirus Infection Probability using Machine Learning4) Project - Image compression using K-means clustering | Color Quantization using K-MeansThis course include topics ---What is Data Science Describe Artificial Intelligence and Machine Learning and Deep Learning Concept of Machine Learning - Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement LearningPython for Data Analysis- Numpy Working envirnment-Google ColabAnaconda Installation Jupyter Notebook Data analysis-PandasMatplotlib What is Supervised Machine LearningRegressionClassification Multilinear Regression Use Case- Boston Housing Price Prediction Save Model Logistic Regression on Iris Flower Dataset Naive Bayes Classifier on Wine Dataset Naive Bayes Classifier for Text Classification Decision TreeK-Nearest Neighbor(KNN) Algorithm Support Vector Machine Algor
Learn Machine Learning and AI with Python Web Apps
Learn Machine Learning (Georgia Tech OMSCS)
Learn Quantum Machine Learning: Theory and Applications
This free course covers the fundamentals of regression analysis, including linear regression, logistic regression, and other advanced techniques. It also provides hands-on coding experience in Python.
Course DescriptionStay ahead in the world of AI - ML with this completely updated course covering . Machine Learning. Deep Learning. Large Language Models (LLMs). Retrieval-Augmented Generation (RAG). AI Agents. Explainable AI (XAI). AutoML using Google Vertex AI This is a hands-on course, designed for active learning. You are encouraged to practice along with the trainer during sessions or immediately after each lecture to build real, practical skills.The content is organized into 21 manageable days, allowing you to learn systematically without feeling overwhelmed. Whether you're a beginner or an experienced professional, you can start from the basics or jump straight to the advanced sections that interest you most.The course is taught by an industry veteran and founder of an AI startup, bringing real-world insights and project-based learning to every module.Running successfully for the past three years, the course has been regularly refreshed to reflect the latest advancements — including cutting-edge topics like Explainable AI, AutoML on Google Vertex, RAG pipelines, and AI Agent frameworks.If you’re looking for a complete, modern, and industry-focused AI learning experience — this is your perfect starting point.Enroll today and build the AI expertise the future demands!What you’ll learn:Build a strong foundation in Machine Learning and Deep Learning conceptsUnderstand and fine-tune Large Language Models (LLMs) for various applicationsDesign and implement Retrieval-Augmented Generation (RAG)</st
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Learn Machine Learning Model Deployment
This video course introduces state-of-the-art first and second-order stochastic gradient methods for solving large-scale optimization problems and reviews their theoretical background on convergence rate analysis.
This course covers the principles required to develop scalable machine learning pipelines, including a section on Recommendation Systems at Scale which discusses graph-networks, link analysis, collaborative filtering, and challenges of sparsity and scalability.
Learn AI and Machine Learning Bootcamp
Welcome to the gateway to your journey into Python for Machine Learning & Deep Learning!Unlock the power of Python and delve into the realms of Machine Learning and Deep Learning with our comprehensive course. Whether you're a beginner eager to step into the world of artificial intelligence or a seasoned professional looking to enhance your skills, this course is designed to cater to all levels of expertise.What sets this course apart?Comprehensive Curriculum: Our meticulously crafted curriculum covers all the essential concepts of Python programming, machine learning algorithms, and deep learning architectures. From the basics to advanced techniques, we've got you covered.Hands-On Projects: Theory is important, but practical experience is paramount. Dive into real-world projects that challenge you to apply what you've learned and reinforce your understanding.Expert Guidance: Learn from industry expert who has years of experience in the field. Benefit from his insights, tips, and best practices to accelerate your learning journey.Interactive Learning: Engage in interactive lessons, quizzes, and exercises designed to keep you motivated and actively involved throughout the course.Flexibility: Life is busy, and we understand that. Our course offers flexible scheduling options, allowing you to learn at your own pace and convenience.Career Opportunities: Machine Learning and Deep Learning are in high demand across various industries. By mastering these skills, you'll open doors to exciting career opportunities and potentially higher earning potential.Are you ready to embark on an exhilarating journey into the world of Python for Machine Learning & Deep Learning? Enroll now and take the first step towards becoming a proficient AI practitioner!
A foundational lecture on the gradient descent algorithm within the context of the highly-rated Machine Learning course.
Unlock the Power of Calculus in Machine Learning, Deep Learning, Data Science, and AI with Python: A Comprehensive Guide to Mastering Essential Mathematical Skills"Are you striving to elevate your status as a proficient data scientist? Do you seek a distinctive edge in a competitive landscape? If you're keen on enhancing your expertise in Machine Learning and Deep Learning by proficiently applying mathematical skills, this course is tailor-made for you.Calculus for Deep Learning: Mastering Calculus for Machine Learning, Deep Learning, Data Science, Data Analysis, and AI using PythonEmbark on a transformative learning journey that commences with the fundamentals, guiding you through the intricacies of functions and their applications in data fitting. Gain a comprehensive understanding of the core principles underpinning Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.Upon mastering the concepts presented in this course, you'll gain invaluable intuition that demystifies the inner workings of algorithms. Whether you're crafting self-driving cars, developing recommendation engines for platforms like Netflix, or fitting practice data to a function, the essence remains the same.Key Learning Objectives:Function Fundamentals: Initiate your learning journey by grasping the fundamental definitions of functions, establishing a solid foundation for subsequent topics.Data Fitting Techniques: Progress through the course, delving into data fitting techniques essential for Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.Approximation Concepts: Explore important concepts related to approximation, a cornerstone for developing robust models in Machine Learning, Deep Learning, Artificial Intelligence, and Data Science.Neural Network Training: Leverage you
This University of London course provides a practical introduction to the K-Means clustering algorithm, with a focus on the underlying statistical concepts.
Obtain skills in one of the most sort after fields of this centuryIn this course, you'll learn how to get started in data science. You don't need any prior knowledge in programming. We'll teach you the Python basics you need to get started. Here are some of the items we will cover in this courseThe Data Science ProcessPython for Data ScienceNumPy for Numerical ComputationPandas for Data ManipulationMatplotlib for VisualizationSeaborn for Beautiful VisualsPlotly for Interactive VisualsIntroduction to Machine LearningDask for Big DataPower BI DesktopGoogle Data StudioAssociation Rule Mining - AprioriDeep Learning Apache Spark for Handling Big DataFor the machine learning section here are some items we'll cover :How Algorithms WorkAdvantages & Disadvantages of Various AlgorithmsFeature ImportancesMetricsCross-ValidationFighting OverfittingHyperparameter TuningHandling Imbalanced DataTensorFlow & KerasAutomated Machine Learning(AutoML)Natural Language ProcessingThe course also contains exercises and solutions that will help you practice what you have learned. By enrolling in this course, you'll have lifetime access to the videos and Notebooks. Purchasing the course also comes with a 30-day money-back guarantee, so you can try it at no risk at all. Let's now add Data Science, Machine Learning, and Deep Learning to your CV. See you inside the course. The course also contains exercises and solutions that will help you practice what you have learned. By enrolling in this course
Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques. Embark on a journey into the fascinating world of deep learning with Python and PyTorch, tailored specifically for image classification tasks. In this hands-on course, you'll delve deep into the principles and practices of deep learning, mastering the art of building powerful neural networks to classify images with remarkable accuracy. From understanding the fundamentals of convolutional neural networks to implementing advanced techniques using PyTorch, this course will equip you with the knowledge and skills needed to excel in image classification projects.Deep learning has emerged as a game-changer in the field of computer vision, revolutionizing image classification tasks across various domains. Understanding how to leverage deep learning frameworks like PyTorch to classify images is crucial for professionals and enthusiasts alike. Whether you're a data scientist, software engineer, researcher, or student, proficiency in deep learning for image classification opens doors to a wide range of career opportunities. Moreover, with the exponential growth of digital imagery in fields such as healthcare, autonomous vehicles, agriculture, and more, the demand for experts in image classification continues to soar.Course Breakdown:You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models. </l
Master Deep Learning with Python for AI ExcellenceCourse Description: This meticulously crafted course is designed to empower you with comprehensive knowledge and practical skills to thrive in the world of artificial intelligence.Immerse yourself in engaging lectures and hands-on lab sessions that cover fundamental concepts, cutting-edge methodologies, and real-world applications of deep learning. Gain expertise in essential Python libraries, machine learning algorithms, and advanced techniques, setting a solid foundation for your AI career.Course Highlights:In-Demand Skills: Acquire the highly sought-after skills demanded by today's AI-centric job market, opening doors to data science, machine learning, and AI development roles.Hands-On Learning: Learn by doing! Our interactive lab sessions ensure you gain practical experience, from data preprocessing to model evaluation, making you a proficient deep learning practitioner.Comprehensive Curriculum: From foundational Python libraries like Pandas and NumPy to cutting-edge neural network architectures like CNNs and RNNs, this course covers it all. Explore linear regression, logistic regression, decision trees, clustering, anomaly detection, and more.Expert Guidance: Our experienced instructors are committed to your success. Receive expert guidance, personalized feedback, and valuable insights to accelerate your learning journey.Project-Based Learning: Strengthen your skills with real-world projects that showcase your <
This course introduces the AI and machine learning offerings on Google Cloud for both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, covering AI foundations, development, and solutions. The course is aimed at data scientists, AI developers, and ML engineers, offering engaging learning experiences and practical hands-on exercises.
Hello there,Welcome to the “Machine Learning Python with Theoretically for Data Science” course.Machine Learning with Python in detail both practically and theoretically with machine learning project for data scienceMachine learning courses teach you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. machine learning, python, data science, machine learning python, python data science, machine learning a-z, python for data science and machine learning bootcamp, python for data science, complete machine learning, machine learning projects,Use Scikit Learn, NumPy, Pandas, Matpl
Are you ready to dive into the world of Machine Learning and Artificial Intelligence? This comprehensive Machine Learning & AI Bootcamp will take you from beginner to advanced, equipping you with the skills to build intelligent applications, automate decision-making, and apply AI to real-world challenges.What You Will LearnUnderstand Machine Learning fundamentals, including supervised and unsupervised learning.Master essential Python libraries like NumPy, Pandas, and Scikit-learn for data analysis.Implement regression and classification models for predictive analytics.Explore ensemble learning techniques such as Random Forest and Gradient Boosting.Work with clustering algorithms for unsupervised learning.Learn dimensionality reduction techniques like PCA and t-SNE.Build Natural Language Processing (NLP) models for text analysis and chatbots.Apply Computer Vision for image recognition and object detection.Understand search and optimization techniques in AI.Develop AI-powered applications using Generative AI and Reinforcement Learning.Work on real-world projects, including AI chatbots, fraud detection, and recommendation systems.Who Is This Course For?Beginners looking to build a solid foundation in Machine Learning and AI.Developers and Data Scientists wanting to implement AI-driven solutions.Tech enthusiasts eager to explore NLP, Computer Vision, and AI automation.Business professionals seeking to leverage AI in decision-making and
Led by GP, a distinguished AI researcher with 11 PubMed publications and a rich academic background from Cornell, UCSF, NIH, and Amherst College, this course spans the essentials of web development to the frontiers of AI technology. Dive into a learning experience with LIVE HELP available Monday to Friday, 9-5, plus additional online support.Our curriculum is in constant evolution, tailored to your feedback and the dynamic landscape of machine learning and AI. This isn't just another bootcamp; it's a bridge from foundational HTML to pioneering in Python 3, Machine Learning, TensorFlow, and beyond into Artificial Intelligence and Recurrent Neural Networks.Designed for rapid learning, we break down complex concepts into manageable steps. Starting from HTML and CSS to Bootstrap and JavaScript, and advancing through Python 3 to data science, machine learning, and AI, we cover ground rapidly but solidly.Expect to delve into:Frontend web technologies: HTML, CSS, Bootstrap, JavaScript, jQueryPython programming essentials and advanced conceptsData Science, including Machine Learning and AI with TensorFlowPractical applications with projects in sentiment analysis, regression, clustering, and neural networksAn exploration of both traditional statistics and machine learning techniquesWith over 170 lectures and 30+ hours of video content, this course is your most comprehensive guide to becoming a proficient Python developer and an AI specialist. You'll get lifetime access to all materials, including lecture Notebooks.This course is perfect for beginners with no prior programming experience, bootcamp graduates looking to tackle real-world projects, and intermediate Python programmers eager to master AI programming. With a 30-day money-back guarantee, there's no risk in taking the leap. Transform your career with the skills to thrive in the era of AI.
This course focuses on uncovering hidden structures from unlabeled data. It covers Principal Component Analysis (PCA) for dimension reduction and popular clustering methods like K-means and hierarchical clustering.
A guided project that teaches how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company, covering data loading, exploratory data analysis, preprocessing, model training, and evaluation.
Google's fast-paced, practical introduction to machine learning. A self-study guide for aspiring machine learning practitioners.
Hello there,Welcome to the “Artificial Intelligence with Machine Learning, Deep Learning ” courseArtificial intelligence, Machine learning python, python, machine learning, Django, ethical hacking, python Bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, DjangoArtificial Intelligence (AI) with Python Machine Learning and Python Deep Learning, Transfer Learning, TensorflowIt’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical modelsAi, TensorFlow, PyTorch, scikit learn, reinforcement learning, supervised learning, teachable machine, python machine learning, TensorFlow python, ai technology, azure machine learning, semi-supervised learning, deep neural network, artificial general intelligenceMachine learning isn’t just useful for predictive texting or smartphone voice recognition Machine learning is constantly being applied to new industries and new problems Whether you’re a marketer, video game designer, or programmer, my course on Udemy is here to help you apply machine learning to your work Data Science Careers Are Shaping The FutureData science experts are needed in almost every field, from government security to dating apps Millions of businesses and government departments rely on big data to succeed and better serve their customers So data science careers are in high demandUdemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for youIf you want to learn one of the
This course covers the best practices for testing machine learning systems. You'll learn how to design and implement tests for data, models, and infrastructure. The course also covers topics such as fairness, privacy, and security in the context of ML testing.
Learn MLOps, experiment tracking, model management, and production ML from industry experts.
Lets learn basics to transform your career. I promise not to exhaust you with huge number of videos.Artificial Intelligence, Machine Learning, Data Science are the most hot skills in the markets which has potential to help you earn highest salary. These skills has potential to turn your financial to better level which can provide you growth and prosperity. Welcome to the most comprehensive Introduction to AI, Machine Learning and Data Science course! An excellent choice for beginners and professionals looking to expand their knowledge on Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised Learning. This is an introductory course for beginners to boost your knowledge. This course gives introduction to to AI, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised learning with real time examples where machine learning can be applied to solve or simplify real world business problems.What you'll learnIntroduction to buzz words like AI, Machine Learning, Data Science and Deep Learning etc.Real time examples where Machine Learning can be used to solve real world business problemsIntroduction to Supervised Learning and Unsupervised LearningIntroduction to Natural Language ProcessingWhy python is popular for Machine LearningPrerequisite: You just need computer or mobile phone with internet connection to access course material.No prerequisites !Happy Learning!
This specialization teaches how to use machine learning for tasks like demand forecasting and predicting product usage. It covers Python libraries for data manipulation and dives into advanced AI techniques like neural networks and random forests for supply chain challenges.
This specialization covers advanced topics in machine learning, including more complex supervised learning models and techniques.
This course covers the basics of unsupervised learning, supervised learning, reinforcement learning algorithms, and generative models with applications in electric power systems.
IntroductionIntroduction of the CourseIntroduction to Machine Learning and Deep LearningIntroduction to Google ColabPython Crash CourseData PreprocessingSupervised Machine LearningRegression AnalysisLogistic RegressionK-Nearest Neighbor (KNN)Bayes Theorem and Naive Bayes ClassifierSupport Vector Machine (SVM)Decision TreesRandom ForestBoosting Methods in Machine LearningIntroduction to Neural Networks and Deep LearningActivation FunctionsLoss FunctionsBack PropagationNeural Networks for Regression AnalysisNeural Networks for ClassificationDropout Regularization and Batch NormalizationConvolutional Neural Network (CNN)Recurrent Neural Network (RNN)AutoencodersGenerative Adversarial Network (GAN)Unsupervised Machine LearningK-Means ClusteringHierarchical ClusteringDensity Based Spatial Clustering Of Applications With Noise (DBSCAN)Gaussian Mixture Model (GMM) ClusteringPrincipal Component Analysis (PCA)What you’ll learnTheory, Maths and Implementation of machine learning and deep learning algorithms.Regression Analysis.Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.Build Artificial Neural Networks and use them for Regression and Classification Problems.Using GPU with Deep Learning Models.Convolutional Neural NetworksTransfer LearningRecurrent Neural NetworksTime series forecasting and classification.AutoencodersGenerative Adversarial NetworksPython from scr
This course focuses on the best practices and tools for deploying, evaluating, monitoring, and operating production machine learning systems on Google Cloud. It provides hands-on practice with Vertex AI Feature Store, including streaming ingestion at the SDK layer. The curriculum is designed to teach learners how to containerize ML workflows for reproducibility and scalability, and how to efficiently manage ML features.
An in-depth exploration of machine learning applications in cybersecurity, focusing on techniques for threat detection and prevention. Participants will gain a solid grounding in machine learning fundamentals, including neural networks, clustering, and support vector machines, tailored specifically for cybersecurity contexts.
A graduate-level course covering the theory and algorithms for optimization in machine learning. It explores topics like convex and non-convex optimization, gradient-based methods, and stochastic optimization.
This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. You will go from zero to mastery!Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If
A specialization covering a range of optimization methods used in machine learning, from foundational concepts to advanced techniques.
Academy of Computing & Artificial Intelligence proudly present you the course "Data Engineering with Python". It all started when the expert team of Academy of Computing & Artificial Intelligence (PhD, PhD Candidates, Senior Lecturers , Consultants , Researchers) and Industry Experts . hiring managers were having a discussion on the most highly paid jobs & skills in the IT/Computer Science / Engineering / Data Science sector in 2021. At the end of the Course you will be able to start your career in Data Mining & Machine Learning. 1) Introduction to Machine Learning - [A -Z] Comprehensive Training with Step by step guidance2) Setting up the Environment for Machine Learning - Step by step guidance [R Programming & Python]3) Supervised Learning - (Univariate Linear regression, Multivariate Linear Regression, Logistic regression, Naive Bayes Classifier, Trees, Support Vector Machines (SVM), Random Forest)4) Unsupervised Learning5) Convolutional Neural Networks - CNN6) Artificial Neural Networks 7) Real World Projects with SourceCourse Learning OutcomesTo provide awareness of (Supervised & Unsupervised learning) coming under Machine Learning (Why we need Data Mining & Machine Learning, What is Data Mining, What is Machine Learning, Traditional Programming Vs Machine Learning, Steps to Solve a Data Mining & Machine Learning Problem, Classification , Clustering)Describe intelligent problem-solving methods via appropriate usage of Machine Learning techniques.To build appropriate neural models from using state-of-the-art python framework.To setup the Environment for Machine Learning - Step by step guidance [R Progra
A graduate-level course in machine learning with a focus on fundamental methodologies and algorithms, including Kernel Methods and Support Vector Machines.
Part of the DeepLearning.AI specialization, this course teaches the core concepts of linear algebra and how they are applied in machine learning and data science.
This course introduces simple and multiple linear regression models, allowing you to assess the relationship between variables in a data set and a continuous response variable.
Data Science , Machine Learning : Ultimate Course For AllCourse Description:Welcome to the ultimate Data Science , Machine Learning course for 2025 – your complete guide to mastering Data Science , Machine Learning from the ground up with real-world examples and hands-on projects.This course is designed for beginners and intermediate learners who want to dive deep into the fields of Data Science , Machine Learning. Whether you’re starting from zero or brushing up your skills, this course will walk you through all the essential concepts, tools, and techniques used in Data Science , Machine Learning today.You’ll begin by understanding the core principles of Data Science , Machine Learning, then move into Python programming, data preprocessing, model training, evaluation, and deployment. With step-by-step explanations and practical exercises, you’ll gain real-world experience in solving problems using Data Science , Machine Learning.By the end of the course, you’ll be fully equipped to handle real projects and pursue career opportunities in Data Science , Machine Learning confidently.Class Overview:Introduction to Data Science , Machine Learning:Understand the principles and concepts of data science and machine learning.Explore real-world applications and use cases of data science across various industries.Python Fundamentals for Data Science:Learn the basics of Python programming language and its libraries for data science, including NumPy, Pandas, and Matplotlib.Master data manipulation, analysis, and visualization techniques using Python.Data Preprocessing and Cleaning:Understan
This course from Google Cloud is designed for business professionals who want to understand how machine learning can be applied to solve business problems. It covers key concepts and use cases of machine learning, including regression.
This course is a non-technical introduction to the basics of machine learning, including supervised learning concepts.
This course covers the end-to-end process of building and maintaining production ML systems. It includes modules on data needs and modeling strategies, which touch upon the importance of choosing the right data storage and handling evolving data, a key consideration when deciding between row, columnar, and vector-based storage.
The third course in the DeepLearning.AI specialization, focusing on the fundamentals of probability and statistics for machine learning and data science.
This course focuses on the practical application of machine learning techniques in Python. It covers a variety of supervised and unsupervised learning methods and their implementation using the scikit-learn library.
Welcome to the most in-depth and engaging Machine Learning & Data Science Bootcamp designed to equip you with practical skills and knowledge for a successful career in the AI field. This comprehensive course is tailor-made for beginners and aspiring professionals alike, guiding you from the fundamentals to advanced topics, with a strong emphasis on Python programming and real-world applications.Become a master of Machine Learning, Deep Learning, and Data Science with Python in this comprehensive bootcamp. This course is designed to take you from beginner to expert, equipping you with the skills to build powerful AI models, solve real-world problems, and land your dream job in 2024.Master the fundamentals of Data Science:Learn how to work with data effectively, from collection and cleaning to analysis and visualization.Master essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and exploration.Discover the power of data preprocessing techniques to enhance your model's performance.Unlock the potential of Machine Learning with Python:Dive into the core concepts of machine learning algorithms, including regression, classification, and clustering.Implement popular ML algorithms using Scikit-learn, the go-to library for ML in Python.Build your own predictive models and evaluate their accuracy with real-world datasets.Launch your career in Data Science and Machine Learning:Gain practical experience by working on real-world projects and case studies.Learn how to deploy your models in production environments to create real-world impact.Prepare for technical interviews and land your dream job with career guidance and tips.Why choose this course:Comprehensive curriculum covering all essential aspects of
This course, part of the Machine Learning Specialization, delves into classification, one of the core areas of machine learning. You'll learn about various classification models, including logistic regression and decision trees, and explore how to handle large-scale classification tasks. The course uses practical case studies like sentiment analysis and loan default prediction to illustrate the concepts.
This course provides a case-study based introduction to the foundational concepts of machine learning.
This course from the University of Colorado Boulder provides a modern take on regression analysis using the R programming language. You will learn about various regression techniques and how to apply them to real-world data.
This course from Johns Hopkins University focuses on the application of multiple regression analysis in the field of public health. You will learn how to analyze and interpret data using regression models.
This course covers the essential concepts of multivariate calculus required for machine learning, including gradient descent and optimization. It is part of the Mathematics for Machine Learning Specialization.
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Learn to implement and evaluate Random Forest models for machine learning tasks using Python.
Welcome to our Python & TensorFlow for Machine Learning complete course. This intensive program is designed for both beginners eager to dive into the world of data science and seasoned professionals looking to deepen their understanding of machine learning, deep learning, and TensorFlow's capabilities.Starting with Python—a cornerstone of modern AI development—we'll guide you through its essential features and libraries that make data manipulation and analysis a breeze. As we delve into machine learning, you'll learn the foundational algorithms and techniques, moving seamlessly from supervised to unsupervised learning, paving the way for the magic of deep learning.With TensorFlow, one of the most dynamic and widely-used deep learning frameworks, we'll uncover how to craft sophisticated neural network architectures, optimize models, and deploy AI-powered solutions. We don't just want you to learn—we aim for you to master. By the course's end, you'll not only grasp the theories but also gain hands-on experience, ensuring that you're industry-ready.Whether you aspire to innovate in AI research or implement solutions in business settings, this comprehensive course promises a profound understanding, equipping you with the tools and knowledge to harness the power of Python, Machine Learning, and TensorFlow.We're excited about this journey, and we hope to see you inside!
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Take your first step towards becoming a data science expert with our comprehensive R programming course. This course is designed for beginners with little or no programming experience, as well as experienced R developers looking to expand their skill set.You'll start with the basics of R programming and work your way up to advanced techniques used in data science. Along the way, you'll gain hands-on experience with popular R libraries such as dplyr, ggplot2, and tidyr.You will learn how to import, clean and manipulate data, create visualizations and statistical models to gain insights and make predictions. You will also learn data wrangling techniques and how to use R for data visualization.By the end of the course, you'll have a solid understanding of R programming and be able to apply your new skills to a wide range of data science projects. You'll also learn how to use R in Jupyter notebook, so that you can easily share your work and collaborate with others.So, if you're ready to take your first step towards becoming a data science expert, this is the course for you! With our hands-on approach and interactive quizzes, you'll be able to put your new skills into practice right away.In this course, you learn:How to install R-PackagesHow to work with R-data typesWhat is R DataFrame, Matrices, Vectors, etc?How to work with DataFramesHow to perform join and merge operations on DataFramesHow to plot data using ggplot2 in R 4Analysis of real-life dataset Covid-19 How this course will help you?This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.
This course covers regression analysis, least squares, and inference using regression models. It also explores special cases of the regression model, such as ANOVA and ANCOVA, and delves into the analysis of residuals and variability.
End-to-end machine learning tutorials covering algorithms, projects, deployment, and interview preparation.
A complete curriculum to learn machine learning in 3 months. Includes math, algorithms, and projects.
You’ve just stumbled upon the most complete, in-depth Neural Networks for Classification course online.Whether you want to:- build the skills you need to get your first data science job- move to a more senior software developer position- become a computer scientist mastering in data science- or just learn Neural Networks to be able to create your own projects quickly....this complete Neural Networks for Classification Masterclass is the course you need to do all of this, and more.This course is designed to give you the Neural Network skills you need to become a data science expert. By the end of the course, you will understand the Multilayer Perceptron Neural Networks for Classification method extremely well and be able to apply them in your own data science projects and be productive as a computer scientist and developer.What makes this course a bestseller?Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Neural Networks for Classification course. It’s designed with simplicity and seamless progression in mind through its content.This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the Multilayer Perceptron (MLP) technique. It's a one-stop shop to learn Multilayer Networks. If you want to go beyond the core content you can do so at any time.What if I have questions?As if this course wasn’t com
This course emphasizes the importance of building reliable machine learning systems. It covers software testing basics applied to the ML domain to enhance the quality of ML applications. The curriculum includes different testing methodologies like unit and integration testing, as well as more advanced techniques designed for machine learning such as behavioral and smoke testing.
A three-day lecture-based course providing engineers with deep knowledge and hands-on experience with machine learning design and modelling techniques in an industrial context.
Learn Professional Machine Learning Engineer Certification
You will build a binary classification machine learning model to predict if a person is looking for a new job or not. You'll go through the end to end machine learning project-- data collection, exploration, feature engineering, model selection, data transformation, model training, model evaluation and model explainability. We will brainstorm ideas throughout each step and by the end of the project you'll be able to explain which features determine if someone is looking for a new job or not.The template of this Jupyter Notebook can be applied to many other binary classification use cases. Questions like -- will X or Y happen, will a user choose A or B, will a person sign up for my product (yes or no), etc. You will be able to apply the concepts learned here to many useful projects throughout your organization!This course is best for those with beginner to senior level Python and Data Science understanding. For more beginner levels, feel free to dive in and ask questions along the way. For more advanced levels, this can be a good refresher on model explainability, especially if you have limited experience with this. Hopefully you all enjoy this course and have fun with this project!
Hello and welcome to the Machine Learning with STATA course. Machine Learning is influencing our daily lives and is one of the most significant aspects of technological advancements. The goal of this course is to provide you with the most up-to-date Machine Learning methodologies using STATA . It will teach you how to think about data science and machine learning in a new way. This is an excellent approach to begin a career in Machine Learning because you will learn some fundamental principles and receive practical experience. I'm thrilled to share what I know about Machine Learning using STATA with you. I assure you that it will be well worth your time and effort, and that you will gain a vital skill.Based on our research this is the only course that uses STATA to apply Machine Learning Models in Credit Risk Scenario. Because we know that many of you are already familiar with STATA or want to be familiar, we chose it as our platform. From the beginning to the finish of the course, we will start from scratch and work together to build new abilities. In this course, we will work together to create a complete data science project utilizing Credit Risk Data from start to finish. For this course, we have information on around 40,000 consumers, including their level of education, age, marital status, where they live, if they own a home, and other pertinent information. We'll get our hands filthy with these numbers and dig deep into them, and you'll be able to practice on your own. Additionally, you will have access to essential resources like as lectures, homework, quizzes, slides, and a literature analysis on modeling methodologies. Let's see what the course structure looks like right now!
This course focuses on optimization techniques with applications in wireless communications, machine learning, and big data.
A course that focuses specifically on regression techniques within supervised learning, offered by IBM.
Build 2 complete projects start to finish -- with each step explained thoroughly by instructor Nimish Narang from Mammoth Interactive.Hands-On Neural Networks: Build Machine Learning Models was funded by a #1 project on KickstarterNimish is our cross-platform developer and has created over 20 other courses specializing in machine learning, Java, Android, SpriteKit, iOS and Core Image for Mammoth Interactive. When he's not developing, Nimish likes to play guitar, go to the gym and laze around at the beach. Project #1 -- Learn to construct a model for credit card fraud detection. Our model will take in a list of transactions, some fraudulent and some legitimate. It will output the percentage at which it can calculate fraudulence and legitimacy, how accurate it is. We will also modify the model so that it output whether a specific transaction is fraudulent or legitimate if we pass them in one by one.We will explore a dataset so that you fully understand it, and we will work on it. It's actually pretty hard to find a dataset of fraudulent/legitimate credit card transactions, but we at Mammoth Interactive have found everything for you and curated a step by step curriculum so that you can build alongside us.We will manipulate the dataset so that it will be easy to feed into our model. We will build a computational graph with nodes and functions to run input through the mini neural network.Machine Learning Projects Using Tensorflow -- Mammoth InteractiveProject #2 -- Learn to build a simple stock market prediction model that will predict whether the price stock will go up or down the next morning based on the amount of volume exchange for a given dayAny kind of glo
Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Artificial Intelligence, Machine Learning, Data Science , Auto Ml, Deep Learning, Natural Language Processing (NLP) Web Applications Projects With Python (Flask, Django, Heruko, Streamlit Cloud).How much does a Data Scientist make in the United States?The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor)Salaries by Company, Role, Average Base Salary in (USD)Facebook Data Scientist makes USD 1,36,000/yr. Analyzed from 1,014 salaries.Amazon Data Scientist makes USD 1,25,704/yr. Analyzed from 307 salaries.Apple Data Scientist makes USD 1,53,885/yr. Analyzed from 147 salaries.Google Data Scientist makes USD 1,48,316/yr. Analyzed from 252 salaries.Quora, Inc. Data Scientist makes USD 1,22,875/yr. Analyzed from 509 salaries.Oracle Data Scientist makes USD 1,48,396/yr. Analyzed from 458 salaries.IBM Data Scientist makes USD 1,32,662/yr. Analyzed from 388 salaries.Microsoft Data Scientist makes USD 1,33,810/yr. Analyzed from 205 salaries.Walmart Data Scientist makes USD 1,08,937/yr. Analyzed 187 salaries.Cisco Systems Data Scientist makes USD 1,57,228/yr. Analyzed from 184 salaries.Uber Data Scientist makes USD 1,43,661/yr. Analyzed from 151 salaries.Intel Corporation Data Scientist makes USD 1,25,930/yr. Analyzed from 131 salaries.Airbnb Data Scientist makes USD 1,80,569/yr. Analyzed from 122 salaries.Adobe Data Scientist makes USD 1,39,074/yr. Analyzed from 109 salaries.<
YOUR COMPLETE GUIDE TO H2O: POWERFUL R PACKAGE FOR MACHINE LEARNING, & DEEP LEARNING IN R This course covers the main aspects of the H2O package for data science in R. If you take this course, you can do away with taking other courses or buying books on R based data science as you will have the keys to a very powerful R supported data science framework. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in machine learning, neural networks and deep learning via a powerful framework, H2O in R, you can give your company a competitive edge and boost your career to the next level!LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I finished a PhD at Cambridge University, UK, where I specialized in data science models. I have +5 years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals.Over the course of my research, I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science... You will go all the way from carrying out data reading & cleaning to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.Among other things:You will be introduced to powerful R-based
This course focuses on topic modeling for marketing data. You will learn to apply topic modeling to various marketing use cases, evaluate and tune topic models, and use them to classify documents. The course covers both traditional and neural network approaches to topic modeling.
Machine learning and Deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains.In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects).Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students' skills in Python language will also increase and they will become more proficient in it.In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, ... and different models. Also, they will use artificial neural networks for modeling to do the projects.The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course.Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains.Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then
A professional certificate program that covers essential machine learning algorithms, including decision trees and ensemble methods.
An industry-focused introduction to machine learning that covers key algorithms, data preparation techniques, and model evaluation strategies. It is ideal for those looking to apply ML in a business context.
Real-Life Machine Learning and Data Science Projects [2025]: Unleash the Future of Data Mastery! Are you ready to embark on an extraordinary voyage into the realm of Real Life Machine Learning and Data Science Projects? Brace yourself for an electrifying experience that will elevate your skills, boost your career prospects, and open the doors to limitless possibilities!What Awaits You in this Cutting-Edge Course:1. Data Empowerment: Navigate the vast data landscape with finesse as you learn to upload datasets in Google Colab and unleash the true potential of your data.2. The Data Sorcerer: Unlock the secrets of data manipulation using the powerful Pandas library, transforming raw data into actionable insights.3. The Data Alchemist: Harness the true power of Google Colab as you embark on thrilling Machine Learning and Data Science Projects that will leave you spellbound.4. Mastering Real-Life Data Challenges: Fearlessly conquer missing values in real-world datasets, both categorical and numerical, becoming a data superhero.5. The Code Whisperer: Unravel the language of data with Label Encoding, empowering you to speak the language of machines fluently.6. Data Splitting Zen: Achieve data harmony through expertly splitting datasets into Training and Testing sets, laying the foundation for brilliant model creation.7. The Model Architect: Build robust models using KNN, Logistic Regression, SVM, and XGBoost Regressor, transforming data into valuable predictions.8. The Art of Data Storytelling: Immerse yourself in the mesmerizing world of Data Visualization using Seaborn and Ma
An introductory course to the concepts and terminology of artificial intelligence (AI) and machine learning (ML). Students will be able to select and apply ML services to resolve business problems and will be able to label, build, train, and deploy a custom ML model.
A guide for developers on how to train a machine learning model and deploy it on-device in an iOS app using Core ML. The tutorial covers the entire process from data collection to model execution in Xcode.
Welcome to our Data Science and Machine Learning course, meticulously crafted for those passionate about leveraging data and developing sophisticated models. This program starts with the fundamentals of data science, where you'll learn to collect, clean, and analyze data using Python libraries like pandas and NumPy. We’ll cover essential data visualization techniques to transform raw data into meaningful insights that drive decision-making.As you advance, we will delve into a range of machine learning algorithms, including both supervised and unsupervised methods. You'll gain hands-on experience with practical applications such as regression, classification, clustering, and dimensionality reduction. Our approach ensures that you not only understand theoretical concepts but also apply them to real-world scenarios through engaging projectsThe culmination of the course involves building a stock prediction tool, allowing you to apply your accumulated knowledge to a practical problem. This final project will showcase your ability to develop, implement, and evaluate predictive models, demonstrating your readiness for real-world challenges. By the end of this course, you'll possess a solid foundation in data science and machine learning, equipping you to tackle complex challenges and make valuable contributions in any industry. Join us to unlock your potential and advance your career in this dynamic and rapidly evolving field!
Are you ready to embark on a data-driven journey into the world of machine learning and data science? If you're looking for a practical yet powerful starting point, then you're in the right place. Linear regression, the simple yet highly popular machine learning algorithm, is your gateway. It's not just jargon; it's a versatile tool used to uncover crucial insights in your data and predict the future.In this hands-on data science and machine learning project, we'll delve into the driving factors behind California house prices. You'll learn how to clean and visualize data, process it, and harness various Python libraries. By the end of this project, you'll have mastered linear regression in Python and gained essential skills for conducting data science projects.What You'll Gain:Mastery of Python Libraries: Dive into data science and machine learning with pandas, Scikit-learn, statsmodels, matplotlib, and seaborn.Real-World Application: Apply your knowledge to a hands-on project that you can showcase on your personal website and resume.Step-by-Step Approach: Follow a clear, concise case study to build your confidence and expertise in machine learning and data science.Start your data science journey with a simple yet strong foundation. Let's get started!This course will empower you to unlock the potential of data science, equipping you with the skills to make informed decisions and drive success in the tech industry.
This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.The course will equip students with a solid understanding of the theory and practical skills necessary to learn machine learning models and data science.When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.This course contains 9 sections: 1. Introduction to Machine Learning 2. Anaconda – An Overview & Installation 3. JupyterLab – An Overview 4. Python – An Overview 5. Linear Algebra – An Overview 6. Statistics – An Overview 7. Probability – An Overview 8. OOPs – An Overview 9. Important Libraries – An OverviewThis course includes 20 lectures, 10 hands-on sessions, and 10 downloadable assets.By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.
Learn the Most demanding language of industry with concept applied to Data Science, Machine Learning and AIImportant topics are covered such as Python Basic Concepts, Advance Concept, Python Crash Course, Python Libraries such as numpy, pandas, matplotlib, seaborn, Data Science Concept with Case Studies , Machine Learning and it's types, Artificial Intelligence with Case Studies This Course will design to understand Data Visualization and Data Analysis with Machine Learning Algorithms with case Studies. Data Analysis with Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered. The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. Data Visualization and Analysis with ML using Python, Numpy Pandas, Matplotlib, Seaborn, Plotly & Scikit Learn libraryThis Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the trad
This course teaches machine learning from the basics so that you can get started with created amazing machine learning programs. With a well structured architecture, this course is divided into 4 modules:Theory section: It is very important to understand the reason of learning something. The need for learning machine learning and javascript in this particular case is explained in this section.Foundation section: In this section, most of the basic topics required to approach a particular problem are covered like the basics of javascript, what are neural networks, dom manipulation, what are tensors and many more such topicsPractice section: In this section, you put your learnt skills to a test by writing code to solve a particular problem. The explanation of the solution to the problem is also provided in good detail which makes hands-on learning even more efficient. Project section: In this section, we build together a full stack project which has some real life use case and can provide a glimpse on the value creation by writing good quality machine learning programsHappy Coding,Vinay Phadnis :)
This course will help you in understanding of the Linear Algebra and math’s behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.There’re tons of topics in this course. To begin the course:We have a discussion on what is Linear Algebra and Why we need Linear AlgebraThen we move on to Getting Started with Python, where you will learn all about how to setup the Python environment, so that it’s easy for you to have a hands-on experience.Then we get to the essence of this course;Vectors & Operations on VectorsMatrices & Operations on MatricesDeterminant and InverseSolving Systems of Linear EquationsNorms & Basis VectorsLinear IndependenceMatrix FactorizationOrthogonalityEigenvalues and EigenvectorsSingular Value Decomposition (SVD)Again, in each of these sections you will find Python code demos and solved problems apart from the theoretical concepts of Linear Algebra.You will also learn how to use the Python's numpy library which contains numerous functions for matrix computations and solving Linear Algebric problems.So, let’s get started….
TensorFlow is the world’s most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as “Eager Execution”. It will support more platforms and languages, improved compatibility and remove deprecated APIs.This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects.Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning.By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.About the AuthorVlad Ionescu is a lecturer at Babes-Bolyai University. He has a PhD in machine learning, a field he is continuously researching and exploring every day with technologies such as Python, Keras, and TensorFlow.His philosophy is “If I can't explain something well enough for most people to understand it, I need to go back and understand it better myself before trying again”. This philosophy helps him to give of his best in his lectures and tutorials.He started as a high school computer science teacher while he was doing his Masters over 5 years ago. Right now, he teaches various university-level courses and tutorials, coverin
This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning!Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning.Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning. However, this fact makes it more difficult for learners to study statistics because they are not sure where to start and what are the most relevant topics of statistics for data science.This course comes to close this gap.This course is designed for both beginners with no background in statistics for data science or for those looking to extend their knowledge in the field of statistics for data science.I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single topic.In this comprehensive course, I will guide you to learn the most common and essential methods of statistics for data analysis and data modeling.My course is equivalent to a college-level course in statistics for data science and machine learning that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With 77 HD video lectures, many exercises, and two projects with solutions.All materials presented in this course are provided in detailed downloadable notebooks for every lecture.Most students focus on learning python codes for data science, however, this is not enough to be a proficient data scientis
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.It gives detailed guide on the Data science process involved and Machine Learning algorithms. All the algorithms are covered in detail so that the learner gains good understanding of the concepts. Although Machine Learning involves use of pre-developed algorithms one needs to have a clear understanding of what goes behind the scene to actually convert a good model to a great model.Our exotic journey will include the concepts of:Comparison between Artificial intelligence, Machine Learning, Deep Learning and Neural Network.What is data science and its need.The need for machine Learning and introduction to NLP (Natural Language Processing).The different types of Machine Learning – Supervised and Unsupervised Learning.Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease.All the important libraries you would need to work on Machine learning lifecycle.Full-fledged course on Statistics so that you don’t have to take another course for statistics, we cover it all.Data cleaning and exploratory Data analysis with all t
Are you looking to ace your next data scientist or data analyst interview? Look no further! This comprehensive Udemy course, "Machine Learning & Data Science Interview Guide: 2025" is designed to equip you with the knowledge and skills necessary to excel in your data science job interviews.600+ Most Asked Interview Questions around Wide topics:Curated selection covering essential topics frequently tested during interviews.Dives deep into various domains, including Python, SQL, Statistics and Mathematics, Machine Learning and Deep Learning, Power BI, Advanced Excel, and Behavioral and Scenario-based questions.Python Section (100 Questions):Tests proficiency in coding with Python.Ensures a strong understanding of this popular programming language.SQL Section (100 Questions):Sharpens SQL querying skills.Tests knowledge of database querying and manipulation.Statistics and Mathematics Section (100 Questions):Solidifies understanding of foundational concepts.Covers essential statistical and mathematical principles.Machine Learning and Deep Learning Section (135 Questions):Explores theoretical knowledge and practical application.Prepares for ML and DL-related interview questions.Power BI and Advanced Excel Sections (105 Questions):Demonstrates expertise in data visualization and analysis tools.Covers a range of topics in Power BI and Advanced Excel functionalities.To round off your interview preparation, the course includes 60 questions that focus on behavioral and scenario-based aspects,
Today we see AI all around us.From apps on our phone, to voice assistants in our room, we have gadgets powered by AI and Machine Learning.If you’re curious to know how machine learning works, or want to get started with this technology, then this course is for you.This is a beginner level course in AI - Machine Learning and Deep Learning.As students, you will gain immensely by knowing about this transformative technology, its potential and how to make the best use of it. It will open up opportunities in your existing jobs as well as prepare you for new careers.It will go over the basic concepts, introduce the terminology and discuss popular Machine Learning and Deep Learning algorithms using examples.It will be ideal for•Students aspiring to begin a career in AI•IT Professionals and Managers who want to understand the basic concepts•Just about anyone who is curious to learn about AIAt the end of this course, you will•Understand the basic concepts and terminologies in Machine Learning•Gain intuition about how various Machine Learning and Deep Learning algorithms work•Learn how to use Machine Learning to solve a business problem•Be able to apply this knowledge to pursue a vendor certificationAre there any pre-requisites?Students must have a basic knowledge of undergraduate level mathematics in areas like Linear Algebra, Probability, Statistics and Calculus. The course will provide a basic refresher on these concepts.How much programming is needed?Although there are labs in the course, they are optional. You can go through the course without doing any programming. However, a basic knowledge of Computer Science and programming would help.The algorithms discussed in the course will be shown using pseudo code.We have an optional mo
A guided project that teaches the fundamentals of using TensorFlow for image classification. You will build, train, and evaluate a neural network to classify images from the CIFAR-10 dataset.
This course explores the transformative role of AI and Machine Learning in modern insurance operations, including underwriting, claims processing, fraud detection, customer engagement, and risk assessment.
Want to dive into Deep Learning and can't find a simple yet comprehensive course?Don't worry you have come to the right place.We provide easily digestible lessons with plenty of programming question to fill your coding appetite. All topic are thoroughly explained and NO MATH BACKGROUND IS NEEDED. This class will give you a head start among your peers.This class contains fundamentals of Image Classification with Tensorflow.This course will teach you everything you need to get started.
This course, part of the Master in Applied Artificial Intelligence program, covers the fundamentals of theoretical statistics that form the foundation for analyzing machine learning algorithms. Topics include statistical models, inference, maximum likelihood estimation, hypothesis testing, and Bayesian inference.
Short Summary about the need and importance of the CourseLinear Algebra is the backbone of Data Science, Machine Learning (ML), and Artificial Intelligence (AI). Understanding its core concepts is essential to grasp the functionality of ML algorithms. However, most courses make this process overwhelming by focusing on complex calculations rather than the practical application you need to understand the working of Machine Learning Algorithms. How our course is different ?We’ve designed this Linear Algebra course specifically for aspiring Data Scientists and Machine Learning enthusiasts who want to dive into the essentials without wasting time. In just around 7.5 hours, you’ll master the key concepts required for Machine Learning, with a clear focus on how these concepts apply directly to real-world Machine Learning algorithms. This Course will teach you the geometric intuition and essential computations so that you can think like a Machine Learning Expert.Please find the Complete Syllabus for the Course belowMathematics for Machine Learning: 1. Introduction to linear AlgebraDifference between Algebra and Linear Algebra, Definition of Linear Algebra, Linear Equation and System of linear equations with an Example, Attributes and properties of system of linear equation.Mathematics for Machine Learning: 2. Geometric representation of an expressionGeometric visualization of an algebraic expression with an example, Gradient of a straight line, Generalization of an expression geometrically on an N dimensional plane.Mathematics for Machine Learning: 3. Importance of a System of linear EquationDefinition and Goal of System of Linear Equations, General form of system of Linear Equations, representing a dataset in terms of System of linear equations, Applications of system of linear equations in solving a classification and a regression problem with an e
Welcome to the best Machine Learning and Data Science with Python course in the planet. Are you ready to start your journey to becoming a Data Scientist?In this comprehensive course, you’ll begin your journey with installation and learning the basics of Python. Once you are ready, the introduction to Machine Learning section will give you an overview of what Machine Learning is all about, covering all the nitty gritty details before landing on your very first algorithm. You'll learn a variety of supervised and unsupervised machine learning algorithms, ranging from linear regression to the famous boosting algorithms. You’ll also learn text classification using Natural Language processing where you’ll deal with an interesting problem.Data science has been recognized as one of the best jobs in the world and it’s on fire right now. Not only it has a very good earning potential, but also it facilitates the freedom to work with top companies globally. Data scientists also gets the opportunity to deal with interesting problems, while being invaluable to the organization and enjoy the satisfaction of transforming the way how businesses make decisions. Machine learning and data science is one of the fastest growing and most in demand skills globally and the demand is growing rapidly. Parallel to that, Python is the easiest and most used programming language right now and that’s the first language choice when it comes to the machine learning. So, there is no better time to learn machine learning using python than today.I designed this course keeping the beginners and those who with some programming experience in mind. You may be coming from the Finance, Marketing, Engineering, Medical or even a fresher, as long as you have the passion to learn, this course will be your first step to become a Data Scientist.I have 20 hours of best quality video contents. There are over 90 HD video lectures each rangi
As part of the Master of Science in Engineering in AI Online program, this area of study offers a deep dive into GPU programming for AI and machine learning. The program is taught by leading AI researchers and covers the technical skills needed for modern deep learning.
Science des Données et Apprentissage Automatique : Compréhension Théorique ApprofondieLa science des données (Data Science) est un domaine vaste et fascinant, tandis que l'apprentissage automatique (Machine Learning) est une branche passionnante de la Data Science. Ce cours de deux heures offre une exploration détaillée de ces domaines pour ceux qui souhaitent comprendre leur fonctionnement.Ce cours se distingue par son approche visuelle et simplifiée, qui démystifie les concepts et algorithmes de l'apprentissage automatique sans se perdre dans les détails mathématiques. Il se concentre sur la théorie, offrant une base solide pour quiconque souhaite exceller dans le domaine de la science des données.Les sections de ce cours sont interconnectées et progressives, formant un ensemble cohérent qui facilite l'apprentissage. Chaque section se construit sur les précédentes, vous permettant d'explorer des concepts de plus en plus avancés au fur et à mesure de votre progression.Ce cours aborde les compétences les plus recherchées dans le monde réel de la science des données et de l'apprentissage automatique. Il est conçu pour être simple, facile à comprendre, et descriptif, vous permettant de progresser rapidement.Rejoignez ce cours pour démystifier la science des données et l'apprentissage automatique. C'est une opportunité unique d'acquérir des connaissances solides dans un format accessible et inspirant !Contenu du cours :Après avoir suivi ce cours avec succès, vous serez en mesure de :Comprendre les concepts, principes et théories de la science des données et de l'apprentissage automatiqueAppréhender la méthodologie de la science des données et de l'apprentissage automatiqueÉvaluer les avantages et les inconvénients des différents algorithmes d'apprentissage automatiqueSélectionner l'algorithme d'apprentissage automat
Welcome to the ultimate ChatGPT and Python Data Science course—your golden ticket to mastering the art of data science intertwined with the latest AI technology from OpenAI.This course isn't just a learning journey—it's a transformative experience designed to elevate your skills and empower you with practical knowledge.With AI's recent evolution, many tasks can be accelerated using models like ChatGPT. We want to share how to leverage AI it for data science tasks.Embark on a journey that transcends traditional learning paths. Our curriculum is designed to challenge and inspire you through:Comprehensive Challenges: Tackle 10 concrete data science challenges, culminating in a case study that leverages our unique 365 data to address genuine machine learning problems.Real-World Applications: From preprocessing with ChatGPT to dissecting a furniture retailer's client database, explore a variety of industries and data types.Advanced Topics: Delve into retail data analysis, utilize regular expressions for comic book analysis, and develop a ChatGPT-powered movie recommendation system. Engage with such critical topics as AI ethics to combat biases and ensure data privacy.This course emphasizes practical application over theoretical knowledge, where you will:Perform dynamic sentiment analysis using a Naïve Bayes algorithm.Craft nuanced classification reports with our proprietary data.Gain hands-on experience with real datasets—preparing you to solve complex data science problems confidently.We’ll be using ChatGPT, Python, and Jupyter Notebook throughout the course, and I’ll link all the datasets, Notebooks for you to play around with on your own.I'll help you create a ChatGPT profile, but I’ll assume you're adept in Python and somewhat experienced in machine learning. Are you ready to dive into the
In this intensive one-hour course, you’ll dive headfirst into the world of machine learning using TensorFlow and Google Colab. No pit stops—just pure acceleration!What You’ll Cover:TensorFlow Basics: Understand the core concepts, from defining layers to training models.Google Colab Mastery: Leverage Colab’s cloud-based environment for seamless development.Data Prep Express: Quickly preprocess your data without detours.Model Construction: Design and build neural networks like a seasoned pro.Training and Evaluation: Witness your model learn, iterate, and fine-tune for optimal performance.Why Take This Course?Speedy Results: Get up to speed in just one hour.Practical Skills: Apply what you learn to real-world problems.No Pit Stops: We’re all about efficiency here!Prerequisites:Basic Python knowledge (if you can write a for loop, you’re set!)Curiosity and a dash of determinationReady to accelerate your ML journey? Buckle up!Whether you’re a data enthusiast, a developer, or a curious learner, this course is your express ticket to mastering machine learning essentials. Let’s hit the road! Your course instructor is me Adam Cole, a professional software engineer with 5 years working on enterprise level applications. Feel free to send me any questions on LinkedIn at Adam Cole Adam Cole BSc MBCS.
¿Te apetece hacer un curso diferente, en el que no solo aprenderás a dominar todos los pasos de un proyecto de Data Science, sino que también te proporcionará un montón de documentos con toda la teoría y el código que verás en las clases? ¿Te interesa tener una guía, en formato visual y también escrito? Este programa es una mezcla entre el formato de un videocurso tradicional y un máster convencional: está pensado para que, clase a clase, vayas almacenando toda una colección de recursos que, sin duda, se convertirá en tu manual de referencia. Aprenderás a estructurar un proyecto desde cero: sabrás cómo empezar y desarrollar cualquier análisis de datos y conocerás a la perfección todas las herramientas que necesitarás durante el proceso, desde simples funciones de carga de datos, hasta completas librerías de Machine Learning. Además, repasarás todos los conceptos clave de estadística y conocerás cómo funcionan los algoritmos de Machine Learning desde el punto de vista matemático, explicados de una forma gráfica y sencilla. No necesitas tener experiencia previa, ¡pero verás cómo al final del curso te conviertes en todo un experto!A día de hoy, encontrarás:Una colección de más de 30 cuadernos y archivos de Python, totalmente documentados.Documentos en PDF con copia de lo que vamos a ver en las pizarras de trabajo.Recursos y referencias útiles.Trucos, consejos y advertencias de errores que se suelen cometer.Además, tendrás acceso a todas las actualizaciones del curso y a los nuevos recursos que se vayan añadiendo, para siempre.
Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.
A nine-module course covering the role of machine learning and AI in financial services. It delves into key methodologies, algorithm selection, and includes case studies with functional code.
Para entender cómo organizaciones como Google, Amazon e incluso Udemy utilizan el Machine Learning y la inteligencia artificial (IA) para extraer el significado y los conocimientos de enormes conjuntos de datos , este curso de Machine Learning te proporciona lo esencial. Según Glassdoor y Indeed, los científicos de datos ganaron un sueldo medio de 120.000 dólares, ¡y eso es solo la norma!Cuando se trata de ser atractivo, los científicos de datos ya lo son. En un mercado laboral altamente competitivo, es difícil retenerlos una vez contratados. Las personas con una mezcla única de formación científica, experiencia informática y capacidad de análisis son difíciles de encontrar.Al igual que los "quants" de Wall Street de los años ochenta y noventa, se espera que los científicos de datos de hoy en día tengan un conjunto de habilidades similares. Las personas con formación en física y matemáticas acudieron a los bancos de inversión ya los fondos de cobertura en aquella época porque pudieron idear algoritmos y métodos de datos novedosos.Dicho esto, la ciencia de los datos se está convirtiendo en una de las ocupaciones más adecuadas para el éxito en el siglo XXI. Se trata de una profesión informatizada, basada en la programación y de naturaleza analítica. Por lo tanto, no es de extrañar que la necesidad de científicos de datos haya preocupado en el mercado laboral en los últimos años.La oferta, en cambio, ha sido bastante restringida. Es un reto conseguir los conocimientos y habilidades necesarios para ser contratado como científico de datos .En este curso, las notaciones y matemáticas la jerga se reducen a lo más básico, cada tema se explica en un lenguaje sencillo, lo que facilita su comprensión. Una vez que tengas en tus manos el código, podrás jugar con él y construir sobre él. El énfasis de este curso está en entender y usar estos algoritmos en el mu
In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques. This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examplesCourse Structure:Creating a Classification Model using Scikit-learnSaving the Model and the standard Scaler Exporting the Model to another environment - Local and Google ColabCreating a REST API using Python Flask and using it locallyCreating a Machine Learning REST API on a Cloud virtual serverCreating a Serverless Machine Learning REST API using Cloud FunctionsBuilding and Deploying TensorFlow and Keras models using TensorFlow ServingBuilding and Deploying PyTorch ModelsConverting a PyTorch model to TensorFlow format using ONNXCreating REST API for Pytorch and TensorFlow ModelsDeploying tf-idf and text classifier models for Twitter sentiment analysisDeploying models using TensorFlow.js and JavaScriptTracking Model training experiments and deployment with MLFLowRunning MLFlow on Colab and DatabricksAppendix - Generative AI - Miscellaneous Topics.OpenAI and the history of GPT modelsCreating an OpenAI account and invoking a text-to-speech model from Python codeInvoking OpenAI Chat Completion, Text Generation, Image Generation models from Python codeCreating a Chatbot with OpenAI API and ChatGPT Model using Python on Google ColabChatGPT, Large Language Models (LLM) and prompt engineeringNew Section : Agent-Mode Model Building and Deployment with GitHub CopilotVibe Coding: Model Development with GitHub Copilot Using a Single Prompt<li
Atenção: nesse curso ainda estão sendo adicionadas aulas! Machine Learning (aprendizado de máquina) é uma área que representa uma evolução nos campos de Ciência da Computação, Análise de Dados, Engenharia de Software e Inteligência Artificial. Nesse curso você aprenderá Machine Learning com a linguagem de Programação Python. Não é preciso ter conhecimento em Python, pois o curso possui uma seção para quem é iniciante na linguagem. Além disso, o curso trata das principais bibliotecas para análise de dados e utilização de técnicas de aprendizado de máquina tais como NumPy, Pandas, scikit-learn e Matplotlib. Também serão explicadas técnicas de aprendizado de máquina para facilitar o entendimento e utilização das mesmas nos exemplos práticos. Todo o curso é 100% em vídeo-aulas, tem direito a certificado e acesso vitalício! Os instrutores Marcos Castro (mais de 12 mil alunos na Udemy) e Gileno Filho (mais de 10 mil alunos na Udemy) irão estar disponíveis para tirar quaisquer dúvidas através do fórum do curso. O que está esperando? Machine Learning é utilizado por empresas ao redor do mundo para facilitar a análise de dados. Vivemos a era do Big Data, o volume de dados produzidos é gigantesco e precisamos de técnicas para automatizar e nos ajudar a encontrar algum padrão nesses dados de forma que possamos resolver os problemas. Aguardamos você no curso!
Java Server Pages (JSP) is a server-side programming technology that enables the creation of dynamic, platform-independent method for building Web-based applications. JSP have access to the entire family of Java APIs, including the JDBC API to access enterprise databases. This tutorial will teach you how to use Java Server Pages to develop your web applications in simple and easy steps.Why to Learn JSP?JavaServer Pages often serve the same purpose as programs implemented using the Common Gateway Interface (CGI). But JSP offers several advantages in comparison with the CGI.Performance is significantly better because JSP allows embedding Dynamic Elements in HTML Pages itself instead of having separate CGI files.JSP are always compiled before they are processed by the server unlike CGI/Perl which requires the server to load an interpreter and the target script each time the page is requested.JavaServer Pages are built on top of the Java Servlets API, so like Servlets, JSP also has access to all the powerful Enterprise Java APIs, including JDBC, JNDI, EJB, JAXP, etc.JSP pages can be used in combination with servlets that handle the business logic, the model supported by Java servlet template engines.Finally, JSP is an integral part of Java EE, a complete platform for enterprise class applications. This means that JSP can play a part in the simplest applications to the most complex and demanding.AudienceThis tutorial has been prepared for the beginners to help them understand basic functionality of Java Server Pages (JSP) to develop your web applications. After completing this tutorial you will find yourself at a moderate level of expertise in using JSP from where you can take yourself to next levels.
Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist. If I can break into data science without a CS or Stat degree I think you can do it too! In this class allow me sharing my journey towards data science and let me help you breaking into data science. Of course it is not fair to say that after taking one course you will be a data scientist. However we need to start some where. A good start and a good companion can take us further.We will definitely discuss Python, Pandas, NumPy, Sk-learn and all other most popular libraries out there. In this course we will also try to de-mystify important complex concepts of machine learning. Most of the lectures will be accompanied by code and practical examples. I will also use “white board” to explain the concepts which cannot be explained otherwise. A good data scientist should use white board for ideation, problem solving. I also want to mention that this course is not designed towards explaining all the math needed to “practice” machine learning. Also, I will be continuously upgrading the contents of this course to make sure that all the latest tools and libraries are taught here. Stay tuned!
This is a hands-on, project-based course designed to help you master the foundations for unsupervised machine learning in Python.We’ll start by reviewing the Python data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You’ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.From there we'll fit, tune, and interpret 3 popular clustering models using scikit-learn. We’ll start with K-Means Clustering, learn to interpret the output’s cluster centers, and use inertia plots to select the right number of clusters. Next, we’ll cover Hierarchical Clustering, where we’ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we’ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.We’ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You’ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.Next, we’ll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We’ll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.Last but not least, we’ll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.<
Learn Machine Learning from scratch, this course for beginners who want to learn the fundamental of machine learning and artificial intelligence. The course includes video explanation with introductions(basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It's highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.The objective of this course is to explain the Machine learning and artificial intelligence in a very simple and way to understand. I strive for simplicity and accuracy with every definition, code I publish. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details. Below is the list of topics that have been covered:Introduction to Machine LearningSupervised, Unsupervised and Reinforcement learningTypes of machine learningPrincipal Component Analysis (PCA)Confusion matrixUnder-fitting & Over-fittingClassificationLinear RegressionNon-linear Regression</
Este curso sobre el lenguaje de programación R está diseñado para aprender desde cero, paso a paso, hasta convertirte en un experto.Todo está explicado mediante ejemplos para facilitar el aprendizajeEstos son los temas tratados en este curso sobre RConfiguración del entornoInstalación de R y RStudioIntroducción a R Operaciones aritméticas, variables, tipos de datos, vectores, operadores de comparación, ayuda y documentaciónMatrices en R Operaciones aritméticas con matrices, selección de elementos, selección por filas y columnas, función factorData Frames en R Creación de Data Frames, dataset, selección y ordenación, exportar e importar datos y tratamiento de valores nulosListas en R Creación y manejo de listasEntrada y salida de datos en R Ficheros CSV, ficheros EXCEL y bases de datosProgramación básica de R Operadores lógicos, condicionales if else, bucle while, bucle for y funcionesProgramación avanzada de R Funciones predefinidas, funciones sobre vectores, funciones anónimas, funciones matemáticas, expresiones regulares, fecha/horaManipulación de datos con R Manipulación de datos con dplyr, operador pipe y limpieza de datos con tidyrVisualización de datos con R Histogramas, scatterplots, barplots, boxplots, gráficos de distribución, límites y dimensionesGráficos interactivos con PlotlyIntroducción a Machine LearningMachine LearningAlgoritmo de regresión lineal Algoritmo de regresión logística Algoritmo de los K vecinos más cercanos Algoritmo de árboles de decisiónAlgo
This is a crash course, but an in-depth course, which will develop you as a Machine learning specialist. Designed with solutions to real life life problems, this will be a boon for your ongoing projects and the organization you work for. Students, Professors and machine learning consultants will find the course interesting, hassle free and up-to-date. Surely, the students will be employable Machine Learning Engineers and data scientists. Given by an enthusiastic and expert professor after testing it in classrooms and projects several times. The students can carry out a number of projects using this course. This exemplary, engaging, enlightening and enjoyable course is organized as seven interesting modules, with abundant worked examples in the form of code executed on Jupyter Notebook. It is important that data is visualized before attempting to carryout machine learning and hence we start the course with a module on data visualization. This is followed by a full blown and enjoyable exposure to Regression covering simple linear regression, polynomial regression, multiple linear regression. Regression is followed by extensive discussions on another important supervised learning algorithms on Classification. We carry out modeling using classification strategies such as logistic regression, Naive Bayes classifier, support vector machine, K nearest neighbor, Decision trees, ensemble learning, classification and regression trees, random forest and boosting - ada boost, gradient boosting. From supervised learning we move on to discuss about unsupervised learning - clustering for unlabelled data. We study the hierarchical, k means, k medoids and Agglomerative Clustering. It is not enough to know the algorithms, but also strategies such as bias variance trade off and curse of dimensionality to be successful in this challenging field of current and futuristic importance. We also carry out Principal Component analysis and Linear discriminant analysis to de
This program includes modules on data exploration and statistical inference, where students perform statistical analysis on real-world datasets and build and validate hypotheses using statistical tests.
This comprehensive course, Machine Learning & Python Data Science for Business and AI, is designed to transform you from a data novice into a proficient practitioner. Whether you're a business professional looking to leverage data driven insights, a student eager to enter the field of AI, or a developer aiming to add powerful new skills to your toolkit, this course provides a clear, practical, and project based path to mastery.I'll skip the heavy, academic theory and dive straight into the practical application of machine learning. You'll learn by doing, building a portfolio of real world projects that are immediately applicable to business and AI challenges. Our focus is on problem-solving using the most popular and powerful tools in the industry: Python, Pandas, NumPy, Scikit-learn, and Matplotlib.By the end of this course, you'll not only understand the core concepts of machine learning but also be able to implement them with confidence. You'll gain a deep understanding of how to collect, clean, and analyze data to make accurate predictions and informed decisions.Why This Course?In today’s data driven world, organizations rely on data science and AI to stay competitive. Understanding how to harness data effectively can help businesses predict trends, optimize operations, and make smarter decisions. This course is specifically tailored to bridge the gap between technical machine learning concepts and practical business applications.What You Will LearnStart with Python fundamentals and learn how to write clean, efficient code for data analysis.Learn how to process, clean, and visualize data using popular Python libraries like Pandas, NumPy, and Matplotlib to extract meaningful insights.Understand core statistical concepts that form the foundation of machine learning, including probability, distributions, and hypoth
Becoming Data Science professional (Data Scientist) is a long journey and need guidance from seasoned Data Science professional (Chief Data Scientist). We are trying to manage the journey such a way that you learn right skills and in the right way. The whole concepts of the course are to make you ready for Data Science projects, mainly in Machine learning and AI projects. You will learn1. Foundation of Machine learning2. Supervised Machine learning - Regression3. Supervised Machine learning - Classifications4. Unsupervised Machine learning (Clustering, KNN, PCA)5. Text Analytics6. Time Series
Unlock the Power of Python for Data Science and VisualizationWelcome to a comprehensive Python programming course tailored by Selfcode Academy for data science and visualization enthusiasts. Whether you're a beginner or looking to expand your skill set, this course will equip you with the knowledge you need.Master the Python Basics:Start from scratch with Python fundamentals.Learn about variables, data types, and the logic behind programming.Explore conditional statements and loops.Dive into essential data structures like lists, tuples, dictionaries, and sets.Discover the world of functions, including powerful lambda functions.Get familiar with Object-Oriented Programming (OOP) concepts.Python's Role in Data Science:Transition to data science seamlessly.Manipulate dates and times using Python's datetime module.Tackle complex text patterns with regular expressions (regex).Harness the power of built-in Python functions.Embrace NumPy for efficient numerical computing.Master Pandas and its data structures, including Series and DataFrames.Acquire data cleaning skills to handle missing values and outliers.Excel at data manipulation with Pandas, including indexing, grouping, sorting, and merging.Dive into data visualization with Matplotlib to create compelling graphs.Advanced Data Science and Visualization:Uncover insights through Exploratory Data Analysis (EDA) techniques.Automate data analysis with Pandas Profiling, DABL, and Sweetviz.Perfect your data cleaning and preprocessing techniques.Craft captivating visualizations using Seaborn.
Machine Learning, BigQuery, TensorBoard, Google Cloud, TensorFlow, Deep Learning have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help engineers, MBA students and young managers learn the fundamentals of big data and data science and their applications in business scenarios. In this course you will learn1. Data Science2. Machine Learning3. BigQuery4. TensorBoard5. Google Cloud Machine Learning6. AI, Machine Learning, Deep Learning Fundamentals7. Analyzing Data8. Supervised and Unsupervised Learning9. Building a Machine Learning Model Using BigQuery 10. Building a Machine Learning Model Using GCP and Tensorboard11. Building your own model for predicting diabetes using Decision Tree
An in-depth introduction to machine learning, covering topics from linear models to deep learning. The syllabus includes on-line algorithms and support vector machines, with practical implementation in Python projects.
This machine learning course will provide you the fundamentals of how companies like Google, Amazon, and even Udemy utilize machine learning and artificial intelligence (AI) to glean meaning and insights from massive data sets. Glassdoor and Indeed both report that the average salary for a data scientist is $120,000. This is the standard, not the exception.Data scientists are already quite desirable. It's difficult to keep them on staff in today's tight labor market. There is a severe shortage of people who possess the rare combination of scientific training, computer expertise, and analytical talents.Today's data scientists are held to the same standards as the Wall Street "quants" of the '80s and '90s. When the need arose for innovative algorithms and data approaches, physicists and mathematicians flocked to investment banks and hedge funds.So, it's no surprise that data science is rising to prominence as a promising career path in the modern day. It is analytic in focus, driven by code, and performed on a computer. As a result, it shouldn't be a shock that the demand for data scientists has been growing steadily in the workplace for the past few years.On the other hand, availability has been low. Obtaining the education and experience necessary to be hired as a data scientist is tough. And that's why we made this course in the first place!Each topic is described in plain English, and the course does its best to avoid mathematical notations and jargon. Once you have access to the source code, you can experiment with it and improve upon it. Learning and applying these algorithms in the real world, rather than in a theoretical or academic setting, is the focus of this course.Each video will leave you with a new perspective that you can implement right away!If you have no background in statistics, don't let that stop you from enrolling in this course; we welcome students of all levels.
A hands-on lab from Google Cloud that walks you through the process of creating a video dataset, training an AutoML video classification model, and deploying it for batch predictions using Vertex AI.
This course explores various ensemble techniques, including bagging, boosting, and stacking, to improve the performance of your machine learning models.
Data Scientist wurde von Glassdoor als Nummer 1 Job gerankt und erzielt laut Indeed einen überdurchschnittlichen Gehalt. Die Karriere im Bereich Data Science ist eine bereichernde Tätigkeit und erlaubt es euch an den größten und interessantesten Herausforderungen der Welt zu arbeiten. Dieser Kurs richtet sich sowohl an Anfänger, die zum ersten Mal mit der Programmiersprache R in Berührung kommen, als auch für erfahrene Entwickler, die ihr Portfolio um Fähigkeiten in Richtung R, Data Sciene und Machine Learning ausbauen wollen! "Perfekter Einstieg in die Sprache R. Zuvor hatte ich keine Kenntnis dieser Sprache. Gut gefällt mir, dass direkt auch Data Science Anwendungen inbegriffen sind, da ich diese beruflich brauche. Top! (★★★★★ D. Mika)Dieser umfangreiche Kurs ist vergleichbar mit anderen Data Science Bootcamps die mehrere tausend Euro kosten. Das alles findest du in über 120 HD Video Lektionen und detaillierten Code Notebooks zu jeder Lektion. Dies macht diesen Kurs zum umfangreichsten Data Science Kurs mit R auf Udemy!Wir werden gemeinsam lernen, wie man mit R programmiert, grandiose Visualisierungen erstellt und mit echten Daten und echte Data Science Fälle umgeht. Dazu verwenden wir R-Studio und das Jupyter Notebook mit R. Hier ist eine Übersicht einiger Themen:Programmieren mit RFortgeschrittene Programmierung in RR Date Frames zur Lösung komplexer Aufgaben verwendenMit R Excel Datein bearbeitenWeb Scraping mit RR mit SQL verbindenGGPlot2 zur Visualisierung verwendenÜbersicht und Einsatz von DplyR und TidyRPlotly für interaktive Visualisierungen verwendenAnalysiere echte Daten an&
This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2020. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.
Neste curso, exploramos o vasto mundo de Data Science e Machine Learning, focando na base lógica e matemática por trás dos principais algoritmos utilizados na área. Veremos como funcionam os principais algoritmos de Regressão, Classificação, Clusterização, NLP, Deep Learning, Regras de Associação, Algoritmos Genéticos, Séries Temporais e muito mais - sem exagerar no "matematiquês". O curso foi pensado de forma a ser o mais democrático possível, servindo como porta de entrada para pessoas que queiram aprender de verdade os principais conceitos antes de entrar no mercado, pessoas que já estejam trabalhando com ciência de dados mas se veem com dificuldades de entender como funcionam os modelos, ou pessoas que simplesmente se interessam pela área e gostariam de aprender como funciona - não necessariamente visando adentrar o mercado. Até por isso, o curso não é tão orientado a código; ao invés de criar código para cada modelo e cada técnica mostrada, ao final do curso há uma seção com alguns projetos da vida real, em que podemos ver tanto como o código é feito, mas, principalmente, como é o raciocínio e as decisões tomadas para resolver problemas de dados.Também trago uma seção bastante rica e dedicada a explicar como se "produtizam" modelos em empresas, falando sobre coisas como deploy, monitoramento, construção de features, pré-processamento, definição de um projeto de ML, expectativa e visão do mercado, progressão de carreira e muito mais!O curso ainda tem um "crash course" de Python, opcional para quem já programa na linguagem, mas valiosa para aqueles que precisam de uma base mais sólida.
This advanced machine learning and deep learning course will cover the following topics:SBERT and BERT: These are pre-trained models that are used for natural language processing tasks such as sentence classification, named entity recognition, and question answering.Sentence Embedding and Similarity Measures: Techniques for representing sentences as numerical vectors, and methods for comparing the similarity between sentences.Clustering: Algorithms for grouping similar data points together, such as k-means and hierarchical clustering.Text Summarization: Techniques for automatically generating a concise summary of a longer text.Question Answering: Techniques for automatically answering questions based on a given text.Image Clustering: Algorithms for grouping similar images together.Image Search: Techniques for searching for images based on their content.Throughout the course, students will work on hands-on projects that will help them apply the concepts they have learned to real-world problems. They will also get an opportunity to implement the latest state of the art techniques in the field to solve various NLP and CV problems.By the end of this course, your confidence will boost in creating and analyzing the Image and Text Processing ML model in Python. You'll have a thorough understanding of how to use Text Data and Image Data modeling to create predictive models and solve real-world business problems.How this course will help you?This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.What makes us qualified to teach you?I am a Ph.D. Scholar
Hello there,Welcome to the “Data Science and Machine Learning Fundamentals [Theory Only]” course.Theorical Course for Data Science, Machine Learning, Deep Learning to understand the logic of Data Science algorithmsMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning describes systems that make predictions using a model trained on real-world data.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, python programming, machine learning python, python for beginners, data science. Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, fri
A comprehensive program designed to prepare you for the field of artificial intelligence and machine learning. It covers designing scalable AI & ML infrastructure, core algorithms, AI agent development, and leveraging cloud-based AI & ML services, specifically through Microsoft Azure. A capstone project simulates real-world challenges.
Unlock the fast track to machine learning mastery with our comprehensive course, "Hands-on Machine Learning in Python & ChatGPT." Dive deep into hands-on tutorials utilizing essential tools like Pandas, Numpy, Seaborn, Scikit-learn, Python, and the innovative capabilities of ChatGPT. This course is designed to guide you seamlessly through every stage of the machine learning process, ensuring a complete workflow that empowers you to tackle tasks such as data cleaning, manipulation, preprocessing, and the development of powerful supervised and unsupervised machine learning models.In this immersive learning experience, gain proficiency in crafting supervised models, including Linear Regression, Logistic Regression, Random Forests, Decision Trees, SVM, XGBoost, and KNN. Unleash the power of unsupervised models like KMeans and DBSCAN for cluster analysis. The course is strategically structured to enable you to navigate through these complex concepts swiftly, effortlessly, and with precision.Our primary objective is to equip you with the skills to build machine learning models from scratch, leveraging the combined strength of Python and ChatGPT. You will not only learn the theoretical foundations but also engage in practical exercises that solidify your understanding. By the end of the course, you'll have the expertise to measure the accuracy and performance of your machine learning models, enabling you to make informed decisions and select the best models for your specific use case.Whether you are a beginner eager to enter the world of machine learning or an experienced professional looking to enhance your skill set, this course caters to all levels of expertise. Join us on this learning journey, where efficiency meets excellence, and emerge with the confidence to tackle real-world machine learning challenges head-on. Fast-track your way to becoming a proficient machine learning practitioner with our dynamic and comprehensive course.
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. You'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and maintain project dependencies.Data
Machine Learning is not just technology—it’s a modern wonder. It powers self-driving cars, recommends your next favorite movie, predicts market trends, and even helps doctors detect diseases earlier.And the best part? You can learn it—easily, enjoyably, and professionally.This course transforms Machine Learning and Data Science from “intimidating tech jargon” into simple, engaging lessons packed with real-world applications, practical coding exercises, and a touch of fun that makes learning addictive.What you’ll master:Effortless data handling with Python’s most powerful libraries—Scikit-Learn, NumPy, Pandas, and Matplotlib.Data visualization that makes patterns and trends leap off the screen to make boring data colourful.Supervised & unsupervised learning explained in easy to understand language, with hands-on coding.Regression, classification, and clustering—built from scratch and applied to real problems.Complete project pipelines from messy raw data to polished, predictive models with performance evaluation.Why this course works:Fun, engaging explanations that make even complex algorithms feel simple.No overwhelming theory dumps—just clear concepts and immediate application.Hands-on projects so you learn by doing, not just watching.Step-by-step guidance so you never feel lost, even if you’re starting fresh.Whether you’re aiming to start a career in AI, add Machine Learning to your professional toolkit, or simply explore one of the most exciting fields of our time—this course will guide you with clarity, confidence, and maybe even a few laughs along the way.By the end of this course, you will:</
A beginner-friendly project on creating a decision tree classifier using the R programming language.
If you're a budding data enthusiast, developer, or even an experienced professional wanting to make the leap into the ever-growing world of machine learning, have you often wondered how to integrate the power of TensorFlow with the vast scalability of Google Cloud? Do you dream of deploying robust ML models seamlessly without the fuss of infrastructure management?Delve deep into the realms of machine learning with our structured guide on "Machine Learning with TensorFlow on Google Cloud." This course isn't just about theory; it's a hands-on journey, uniquely tailored to help you utilize TensorFlow's prowess on the expansive infrastructure that Google Cloud offers.In this course, you will:Develop foundational models such as Linear and Logistic Regression using TensorFlow.Master advanced architectures like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) for intricate tasks.Harness the power and convenience of Google Cloud's Colab to run Python code effortlessly.Construct sophisticated Jupyter notebooks with real-world datasets on Google Colab and Vertex.But why dive into TensorFlow on Google Cloud? As machine learning solutions become increasingly critical in decision-making, predicting trends, and understanding vast datasets, TensorFlow's integration with Google Cloud is the key to rapid prototyping, scalable computations, and cost-effective solutions.Throughout your learning journey, you'll immerse yourself in a series of projects and exercises, from constructing your very first ML model to deploying intricate deep learning networks on the cloud.This course stands apart because it bridges the gap between theory and practical deployment, ensuring that once you've completed it, you're not just knowledgeable but are genuinely ready to apply these skills in real-world scenarios.Take the next step in your machi
Module-1Welcome to the Pre-Program Preparatory ContentSession-1:1) Introduction2) Preparatory Content Learning ExperienceMODULE-2INTRODUCTION TO PYTHONSession-1:Understanding Digital Disruption Course structure1) Introduction2) Understanding Primary Actions3) Understanding es & Important PointersSession-2:Introduction to python1) Getting Started — Installation2) Introduction to Jupyter NotebookThe Basics Data Structures in Python3) Lists4) Tuples5) Dictionaries6) SetsSession-3:Control Structures and Functions1) Introduction2) If-Elif-Else3) Loops4) Comprehensions5) Functions6) Map, Filter, and Reduce7) SummarySession-4:Practice Questions1) Practice Questions I2) Practice Questions IIModule-3Python for Data ScienceSession-1:Introduction to NumPy1) Introduction2) NumPy Basics3) Creating NumPy Arrays4) Structure and Content of Arrays5) Subset, Slice, Index and Iterate through Arrays6) Multidimensional Arrays7) Computation Times in NumPy and Standard Python Lists8) SummarySession-2:Operations on NumPy Arrays1) Introduction2) Basic Operations3) Operations on Arrays4) Basic Linear Algebra Operations5) SummarySession-3:Introduction to Pandas1) Introduction2) Pandas Basics3) Indexing and Selecting Data4) Merge and Append5) Grouping and Summarizing Data frames6) Lambda function & Pivot tables7) SummarySession-4:Getting and Cleaning Data1) Introduction2) Reading Delimited and Relational Databases3) Reading Data from Websites4) Getting Data from APIs5) Reading Data from PDF Files6) Cl
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary.This makes Data Science a highly lucrative career choice. It is mainly due to the dearth of Data Scientists resulting in a huge income bubble.Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics, and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.A Data Scientist enjoys a position of prestige in the company. The company relies on its expertise to make data-driven decisions and enable them to navigate in the right direction.Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales.A healthcare company will require data scientists to help them analyze genomic sequences. The salary of a Data Scientist depends on his role and type of work he has to perform. It also depends on the size of the company which is based on the amount of data they utilize.Still, the pay scale of Data scientists is way above other IT and management sectors. However, the salary observed by Data Scientists is proportional to the amount of work that they must put in. Data Science needs hard work and requires a person to be thorough with his/her skills.Due to several luc
Course DescriptionThis tutorial course is a practical, project driven introduction to Machine Learning and Deep Learning using PyTorch. Each concept is taught through real world examples, allowing professionals to quickly understand, how models work and how they are used in real applications. You will build complete end to end projects such as LSTM based sentiment analysis, RNN based spam detection, CNN models for image classification, MLP networks for video quality prediction, and regression models using real datasets from sales, finance, and home loan scenarios. This tutorial course also covers how to convert Jupyter Notebook experiments into a clean, modular Python project structure suitable for production use.By combining NLP, computer vision, and predictive analytics use cases, this tutorial course helps you gain solid practical experience in PyTorch while learning how to preprocess data, design model architectures, train models, evaluate results, and prepare solutions for real-world implementation.This Tutorial Course Primarily Focuses On:Building ML & DL models end to end in PyTorchPerforming data preprocessing and feature engineeringTraining, evaluating, and deploying models with real datasetsUnderstanding architectures like LSTM, CNN, DNN, Decision Trees, Random Forest & MLPConverting research notebooks into production ready Python modulesBy the end of this course, You will be able toBuild machine learning regression & classification modelsDevelop CNN, RNN, MLP, and LSTM architectures in PyTorchPerform NLP tasks like sentiment analysis & spam detectionImplement image classification models for handwritten alphabets & traffic signsConvert notebooks into modular Python project structuresWork with real time data for prediction and quality assessmentYou will learn in this tutorial courseDec
The course Fundamentals Data Science and Machine Learning is a meticulously designed program that provides a comprehensive understanding of the theory, techniques, and practical applications of data science and machine learning. This immersive course is suitable for both beginners and experienced professionals seeking to enhance their knowledge and skills in this rapidly evolving field.Greetings, Learners! Welcome to the Data Science and Machine Learning course. My name is Usama, and I will be your instructor throughout this program. This comprehensive course consists of a total of 9 lectures, each dedicated to exploring a new and crucial topic in this field.For those of you who may not possess prior experience or background knowledge in Data Science and Machine Learning, there is no need to worry. I will commence the course by covering the fundamentals and gradually progress towards more advanced concepts.Now, let's delve into the course outline, which encompasses the following key areas:Data Science: We will dive into the interdisciplinary field of Data Science, exploring techniques and methodologies used to extract meaningful insights from data.Artificial Intelligence: This topic delves into the realm of Artificial Intelligence (AI), where we will explore the principles and applications of intelligent systems and algorithms.Deep learning: Subfield of machine learning that focuses on training artificial neural networks to learn and make predictions from complex and large-scale data. This course provides an overview of deep learning, covering key concepts, algorithms, and applications.Machine Learning: We will extensively cover Machine Learning, which forms the backbone of Data Science, enabling computers to learn and make predictions from data without being explicitly programmed.Data Engineering: This area focuses on the
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python and R. Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. From introductory SQL for data querying to advanced techniques in web scraping for data retrieval, you'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll l
A hands-on project where you'll train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This is a practical skill for media companies to automatically predict the authenticity of news articles.
Learn how to use Numpy and Pandas for Data Analysis. This will cover all basic concepts of Numpy and Pandas that are useful in data analysis.Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.Learn about different Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression etc... All the video sessions will first discuss the basic theory concept behind these algorithms followed by the practical implementation.Learn to how to choose the best hyper parameters for your Machine Learning Model using GridSearch CV. Choosing the best hyper parameters is an important step in increasing the accuracy of your Machine Learning Model.You will learn to build a complete Machine Learning Pipeline from Data collection to Data Preprocessing to Model Building. ML Pipeline is an important concept that is extensively used while building large scale ML projects.This course has two projects at the end that will be built using all concepts taught in this course. The first project is about Diabetes Prediction using a classification machine learning algorithm and second is about prediciting the insurance premium using a regression machine learning algorithm.
< Step-by-step explanation of more than 7 hours of video lessons on Supervised Machine Learning: Complete Masterclass [2023]><Instant reply to your questions asked during lessons><Weekly live talks on Supervised Machine Learning: Complete Masterclass [2023]. You can raise your questions in a live session as well><Helping materials like notes, examples, and exercises><Solution of quizzes and assignments> Welcome to the Machine Learning course!In this comprehensive course, you will learn the fundamental concepts and techniques used in Machine Learning. We will cover a range of topics from data preprocessing to model evaluation and selection, with hands-on exercises and projects to help you build and solidify your understanding of the concepts.The course is designed for beginners, but it will also be valuable for those who have some experience in programming and data analysis. You will be guided through the basics of Python programming and the most commonly used libraries for data manipulation and visualization, such as Pandas and Matplotlib.Once you have mastered the basics, we will delve into the core concepts of Machine Learning, including supervised and unsupervised learning, decision trees, random forests, clustering, neural networks, and deep learning. You will learn how to preprocess data, train and evaluate models, and optimize them for better performance.In addition to the theory, you will also have hands-on practice using real-world datasets and implementing Machine Learning algorithms with Python. By the end of the course, you will be able to apply Machine Learning techniques to solve a wide range of problems and use cases, and have the skills to further your studies in this exciting and rapidly growing field.Whether you are a student, a researcher, or a professional
Recent UpdatesJuly 2024: Added a video lecture on hybrid approach (combining clustering and non clustering algorithms to identify anomalies)Feb 2023: Added a video lecture on "Explainable AI". This is an emerging and a fascinating area to understand the drivers of outcomes. Jan 2023: Added anomaly detection algorithms (Auto Encoders, Boltzmann Machines, Adversarial Networks) using deep learningNov 2022: We all want to know what goes on inside a library. We have explained isolation forest algorithm by taking few data points and identifying anomaly point through manual calculation. A unique approach to explain an algorithm!July 2022: AutoML is the new evolution in IT and ML industry. AutoML is about deploying ML without writing any code. Anomaly Detection Using PowerBI has been added. June 2022: A new video lecture on balancing the imbalanced dataset has been added.May 2022: A new video lecture on PyOD: A comparison of 10 algorithms has been addedCourse DescriptionAn anomaly is a data point that doesn’t fit or gel with other data points. Detecting this anomaly point or a set of anomaly points in a process area can be highly beneficial as it can point to potential issues affecting the organization. In fact, anomaly detection has been the most widely adopted area with in the artificial intelligence - machine learning space in the world of business. As a practitioner of AI, I always ask my clients to start off with anomaly detection in their AI journey because anomaly detection can be applied even when data availability is limited.Anomaly detection can be applied in the following areas:Predictive maintenance in the manufacturing ind
The future world is the AI era of machine learning, so mastering the application of machine learning is equivalent to getting a key to the future career. If you can only learn one tool or algorithm for machine learning or building predictive models now, what is this tool? Without a doubt, that is Xgboost! If you are going to participate in a Kaggle contest, what is your preferred modeling tool? Again, the answer is Xgboost! This is proven by countless experienced data scientists and new comers. Therefore, you must register for this course!The Xgboost is so famous in Kaggle contests because of its excellent accuracy, speed and stability. For example, according to the survey, more than 70% the top kaggle winners said they have used XGBoost.The Xgboost is really useful and performs manifold functionalities in the data science world; this powerful algorithm is so frequently utilized to predict various types of targets – continuous, binary, categorical data, it is also found Xgboost very effective to solve different multiclass or multilabel classification problems. In addition, the contests on Kaggle platform covered almost all the applications and industries in the world, such as retail business, banking, insurance, pharmaceutical research, traffic control and credit risk management.The Xgboost is powerful, but it is not that easy to exercise it full capabilities without expert’s guidance. For example, to successfully implement the Xgboost algorithm, you also need to understand and adjust many parameter settings. For doing so, I will teach you the underlying algorithm so you are able to configure the Xgboost that tailor to different data and application scenarios. In addition, I will provide intensive lectures on feature engineering, feature selection and parameters tuning aiming at Xgboost. So, after training you should also be able to prepare the suitable data or features that can well feed the XGBoost model.This course is really practical but not lacking in theory; w
This course equips Python developers with the foundational NumPy skills essential for data science and machine learning. You’ll move beyond basic lists to master high-performance ndarrays: creating, reshaping, indexing, slicing, and performing vectorized operations — all without slow loops. Learn key concepts like shape, dtype, axis, and the powerful broadcasting mechanism that makes NumPy so efficient. Through hands-on examples (e.g., analyzing grades or sensor data), you’ll gain confidence in mathematical computation, array manipulation, and data preparation. By the end, you’ll seamlessly integrate NumPy with Pandas and scikit-learn — setting the stage for real-world DS/ML workflows. No advanced math needed — just core Python (variables, loops, functions) and a willingness to practice. Includes setup guides, Jupyter notebooks, and practical exercises. Whether you're a student, career-switcher, or self-learner, this is your essential first step into the data ecosystem.هذه الدورة مُعدَّة لمُطوري بايثون لإتقان NumPy — حجر الأساس في علم البيانات وتعلم الآلة. ستنتقل من استخدام القوائم العادية إلى إنشاء ومعالجة المصفوفات عالية الأداء (ndarray) بثقة: التشكيل (reshape)، الفهرسة الذكية، العمليات المتجهية (بدون حلقات بطيئة)، وفهم الخصائص مثل shape وdtype وaxis. ستتعلم مفهوم الـ Broadcasting السحري الذي يجعل العمليات سريعة ومرنة، عبر أمثلة واقعية (مثل تحليل درجات طلاب أو بيانات مناخية). كما ستُجهّز البيانات للانتقال السلس إلى أدوات مثل Pandas و scikit-learn. لا تحتاج إلى خلفية رياضية متقدمة — يكفي أن تعرف أساسيات بايثون (متغيرات، حلقات، دوال). تشمل الدورة شرحًا خطوة بخطوة، دفاتر جوبيتر جاهزة، وتمارين تطبيقية. سواء كنت طالبًا أو تُغيّر مسارك المهني، فهذه الدورة هي بداية رحلتك العملية في عالم البيانات.
Uniform modeling (i.e. models from a diverse set of libraries, including scikit-learn, statsmodels, and tensorflow), reporting, and data visualizations are offered through the Scalecast interfaces. Data storage and processing then becomes easy as all applicable data, predictions, and many derived metrics are contained in a few objects with much customization available through different modules.The ability to make predictions based upon historical observations creates a competitive advantage. For example, if an organization has the capacity to better forecast the sales quantities of a product, it will be in a more favorable position to optimize inventory levels. This can result in an increased liquidity of the organizations cash reserves, decrease of working capital and improved customer satisfaction by decreasing the backlog of orders. In the domain of machine learning, there’s a specific collection of methods and techniques particularly well suited for predicting the value of a dependent variable according to time, ARIMA is one of the important technique.LSTM is the Recurrent Neural Network (RNN) used in deep learning for its optimized architecture to easily capture the pattern in sequential data. The benefit of this type of network is that it can learn and remember over long sequences and does not rely on pre-specified window lagged observation as input. The scalecast library hosts a TensorFlow LSTM that can easily be employed for time series forecasting tasks. The package was designed to take a lot of the headache out of implementing time series forecasts. It employs TensorFlow under-the-hood. Some of the features are:Lag, trend, and seasonality selectionHyperparameter tuning using grid search and time seriesTransformationsScikit models ARIMALSTMMultivariate- Assignment
Learn Python for Data Science and Machine Learning Bootcamp
Learn To Master Data Science And Machine Learning Without Coding And Earn a 6-Figure Income Why Data Science and Machine Learning are the Hottest and Most In-Demand Technology Jobs. Data Scientist was recently dubbed “The Sexiest Job of the 21st Century” by Harvard Business Review, and for good reason! If you’re looking for a fast and effective way to earn a 6-figure income without spending thousands of dollars in training, keep reading to learn about this revolutionary Udemy course. Glassdoor reports that Data Scientist was named the “Best Job in America for 2016,” which was based on the huge amount of career opportunities and 6-figure average salary. Business media from Forbes to The New York Times also frequently report about the increasing demand for data scientists. Why is this great news for you? The sudden increase in demand for Data Scientists has created an incredible skills gap in the job market. According to a McKinsey Report, by the end of 2018 the demand for them is expected to be 60% higher than the available talent! Machine Learning is the Key to Your High-Earning FutureLeading companies understand that Machine Learning is the future, and are investing millions of dollars into Machine Learning Research. Machine Learning is the subset of Artificial Intelligence (AI) that enables computers to learn and perform tasks they haven’t been explicitly programmed to do. Data Scientists and Machine Learning Engineers who are skilled in Machine Learning are even higher in demand across the entire employment spectrum. Many diverse industries are searching for innovation in the field, and their need for Machine Learning experts and engineers is rapidly increasing. Traditional Machine Learning requires students to know software programming, which enables them to write machine le
This course is an exciting hands-on view of the fundamentals of Data Science and Machine LearningData Science and Machine Learning are developing on a massive scale. Everywhere you look in society, the world wide web, or in technology, you will find Data Science and Machine Learning algorithms working behind the scenes to analyze and optimize all aspects of our lives, businesses, and our society. Data Science and Machine Learning with Artificial Intelligence are some of the hottest and fastest-developing areas right now. This course will teach you the fundamentals of Data Science and Machine Learning. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, and aspires to be one of the best Udemy courses in terms of education and value. You will learn aboutRegression and Prediction with Machine Learning models using supervised learning. This course has the most complete and fundamental master-level regression analysis content packages on Udemy, with hands-on, useful practical theory, and automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.Classification with Machine Learning models using supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifier Ensembles and Voting Classifier Ensembles.Cluster Analysis with Machine Learning models using unsupervised learning. In this part of the course, you will learn about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and seven useful Machine Learning clustering algorithms ranging from hierarchical cl
A 2-hour project-based course where you will learn to perform anomaly detection using PyCaret, a low-code machine learning library in Python.
Master Data Science Workflows with H2O: From Prep to Deployment & Generative AI with Michelle Tanco and Jon Farland!This course equips you with H2O's suite of cutting-edge tools, such as Driverless AI, H2O Actions, the Wave App, Gen AI AppStore, LLM DataStudio, H2O LLMStudio, Enterprise GPTe, h2oGPT, and Eval Studio. In this comprehensive course, you will develop a thorough understanding of data preparation and visualization using H2O's intuitive tools, enabling you to efficiently clean, transform, and explore data to uncover actionable insights without the traditional complexities of data wrangling. Dive deep into automated machine learning mastery with Driverless AI, leveraging its automation capabilities to streamline model building processes, allowing you to focus on strategic analysis and solving complex problems effectively. Gain expertise in seamless model deployment techniques, ensuring that your models translate into impactful business outcomes with ease and efficiency. Explore the best of what generative AI has to offer with Enterprise GPTe and H2OGPT, where you will delve into advanced tasks such as text generation, language translation, and creative content development, empowering you to innovate and excel in data science and business decision-making. Join us on this transformative journey to elevate your skills and harness the full potential of H2O's tools for driving data-driven insights and strategic business success.Come aboard our dynamic course and elevate your data science skills!
No Prior Experience Needed – Learn with Real Projects!Are you curious about Data Science & Machine Learning but don’t know where to start? This beginner-friendly bootcamp is your perfect first step! We’ll guide you from absolute zero to building real-world projects—no math or coding background required!What You’ll Learn:Python for Beginners – Learn from scratch with easy-to-follow examples Data Science Essentials – Pandas, NumPy, and data visualization (Matplotlib & Seaborn) Machine Learning Made Simple – Predict trends, classify data & uncover patternsHands-On Projects – Work with real datasets (sales predictions, customer behavior, and more!)AI & ChatGPT Basics – Get introduced to cutting-edge tools like LLMs (Large Language Models) Why This Course?Perfect for Beginners – Starts slow, explains every step, and builds confidenceLearn by Doing – No boring theory—just fun, practical projects you can showcaseNo Experience Needed – We teach Python & math basics along the way Supportive Community – Get help whenever you’re stuck Certificate of Completion – Boost your resume with a valuable skill Who Is This For? Total beginners who want to explore Data Science & AI Students & professionals looking for a high-income skill Career changers curious about tech jobs Anyone who wants to future-proof their skills in 2025! Tools You’ll Use (No Setup Hassle!): Python (easy-to-learn) Jupyter Notebooks (user-friendly coding) Scikit-Learn (simple ML models) ChatGPT & AI tools (see how they work!) Bonus: Downloadable exercises & solutions Cheat sheets & study guides Lifetime access & updates Start Your Data Science Journey Today – No Experience Needed!
Machine Learning é uma disciplina da área da Inteligência Artificial que, por meio de algoritmos, dá aos computadores a capacidade de identificar padrões em dados massivos e fazer previsões (análise preditiva).Data Science é o estudo disciplinado dos dados e informações inerentes ao negócio e todas as visões que podem cercar um determinado assunto. É uma ciência que estuda as informações, seu processo de captura, transformação, geração e, posteriormente, análise de dados para converter em evidência.A Análise de Dados é um processo de inspeção, limpeza, transformação e modelagem de dados com o objetivo de descobrir informações úteis, informar conclusões e apoiar a tomada de decisões. A análise de dados tem múltiplas facetas e abordagens, abrangendo diversas técnicas sob uma variedade de nomes, e é usada em diferentes domínios dos negócios, ciências e ciências sociais. No mundo dos negócios de hoje, a análise de dados desempenha um papel tornando a tomada de decisões mais científicas e ajudando as empresas a operar com mais eficáciaNeste curso você vai entender que juntos a Data Science, Machine Learning e Data Analytics além de inovações tecnológicas são aliados para o bom funcionamento das ações organizacionais, e tem poder de influência em toda cadeia produtiva.Bons estudos!
In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail.It’s important to know both the advantages and disadvantages of each algorithm we look at.Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.We’ll see how we can transform the Bayes Classifier into a linear and
Der Bedarf an Data-Experten wächst wesentlich schneller als das Angebot an Fachkräften. 2022 fehlten laut einer repräsentativen Bitkom-Umfrage rund 137.000 IT-Fachkräfte in Deutschland. Damit liegt der Mangel sogar noch höher als vor der Pandemie.Die Karriere im Bereich Data Science bietet nicht nur finanzielle Vorteile, sondern auch die Möglichkeit, an den herausforderndsten und faszinierendsten Aufgaben der Welt zu arbeiten. Bist du bereit, den Weg als Data Scientist einzuschlagen? "Perfektes Niveau, motivierend und verständlich/gründlich erklärt." (★★★★★ P. Fuchs)Dieser Grundlagenkurs richtet sich sowohl an Anfänger, die zum ersten Mal mit Data Science in Berührung kommen, als auch an Entwickler, die ihr Portfolio um Fähigkeiten in Richtung Data Science und Machine Learning ausbauen wollen!Wichtig: Unser DataScience-Kurs erfordert Grundkenntnisse der Programmierung mit Python! Falls du die Grundlagen von Python bisher noch nicht erlernt hast, solltest du zuerst einen unserer Python-Kurse durcharbeiten!Dieser umfassende Kurs ist inhaltlich vergleichbar mit anderen Data Science Bootcamps, die sonst mehrere tausend Euro kosten. Nun kannst Du all das zu einem Bruchteil der Kosten lernen. Und dank der Plattform Udemy lernst Du wann und wo Du möchtest. Mit über 100 HD Video Lektionen und den detaillierten Code Notebooks zu jeder Lektion ist dies einer der umfangreichsten deutschsprachigen Kurse für Data Science und Maschinelles Lernen (Machine Learning) auf Udemy!Wir bringen dir bei, wie man Python zur Analyse von Daten einsetzt, wie man Daten visualisiert und wie Python zum Maschinellen Lernen (Machine Learning) genutzt werden kann! Hier sind einige der Punkte die wir behandeln werd
This course teaches how to implement machine learning use cases for marketing in Python, including predicting customer churn, measuring and forecasting customer lifetime value, and building customer segments.
Este curso pretende ser una introducción a las técnicas más relevantes de Machine Learning y mostrar ejemplos de aplicación de estas técnicas. Que sirva para conocer qué técnicas existen, en qué se fundamentan y sobre qué tipos de problemas pueden aplicarse. El enfoque será teórico-práctico y se hará uso del lenguaje de programación Python y del toolkit Scikit Learn. Se recomienda a los alumnos instalarse ANACONDA en su plataforma habitual. ANACONDA incluye Python, Scikit-Learn y Matplotlib. La versión de python que utilizaremos será la 3.6.También veremos pyspark como plataforma de desarrollo de aplicaciones distribuídasEntre los principales objetivos podemos destacar:Introducir los conceptos de ciencias de datos y machine learning.Introducir las principales librerías que podemos encontrar en python para aplicar técnicas de machine learning a los datos.Introducir las principales librerías que podemos encontrar en python para tratamiento y visualización de datos Dar a conocer los pasos para construir un modelo de machine learning, desde la adquisición de datos,pasando por la generación de funciones, hasta la selección de modelos.Dar a conocer los principales algoritmos para resolver problemas de machine learning.Introducir scikit-learn como herramienta para resolver problemas de machine learning.Introducir pyspark como herramienta para aplicar técnicas de big data y map-reduce a los datos.Conocer y aplicar algoritmos de machine learning con pyspark.Introducir los sistemas de recomendación basados en contenidos
Welcome to this course on Data Science and Machine Learning with Microsoft Azure. You would learn various lessons for Data Visualization, Data Cleaning and Data Analysis using Microsoft Power BI. It is a powerful Business Intelligence software that can be used for various domains ranging from creating Analytics dashboard and Business Intelligence reports to fetching information from wide range of data sources. You could also perform various types of data cleaning operations using Power Query. Moreover, if you want to create some advanced types of Analytics charts you can write a few lines of code in python using frameworks such as Matplotlib and Seaborn. And if you want to modify the dataset either by creating derived values based on certain mathematical formula or specified conditions you could perform various Data Modelling operations as well by using creating Calculated fields and by using Power Query editor. In this course you would learn various such concepts with completely practical examples on Power BI Desktop, that can be applied in the similar way on azure cloud.After you have learned various lessons on Power BI, you would be learning Azure Machine learning in the later sections of this course. Here you would learn to analyze an image using Computer Vision. And you would also learn to perform language detection, sentiment analysis, key phrase extraction and entity recognition using Azure Text Analytics. Here in this course you will learn following lessons on Data Science using Microsoft Power BI-Creating Visualization charts such as Bar chartPie chartDonut or Ring chartTreemap chartInteractive charts and Drill downTable and MatrixDate and other SlicersCreating a calculated fieldGauge chartMap chart and modesScatterplot and Animation PlaybackBasics of Power QueryRow deletion and
Machine Learning is one of the hottest technologies of our time! If you are new to ML and want to become a Data Scientist, you need to understand the mathematics behind ML algorithms. There is no way around it. It is an intrinsic part of the role of a Data Scientist and any recruiter or experienced professional will attest to that. The enthusiast who is interested in learning more about the magic behind Machine Learning algorithms currently faces a daunting set of prerequisites: Programming, Large Scale Data Analysis, mathematical structures associated with models and knowledge of the application itself. A common complaint of mathematics students around the world is that the topics covered seem to have little relevance to practical problems. But that is not the case with Machine Learning.This course is not designed to make you a Mathematician, but it does provide a practical approach to working with data and focuses on the key mathematical concepts that you will encounter in machine learning studies. It is designed to fill in the gaps for students who have missed these key concepts as part of their formal education, or who need to catch up after a long break from studying mathematics.Upon completing the course, students will be equipped to understand and apply mathematical concepts to analyze and develop machine learning models, including Large Language Models.
Do you want to super charge your career by learning the most in demand skills? Are you interested in data science but intimidated from learning by the need to learn a programming language?I can teach you how to solve real data science business problems that clients have paid hundreds of thousands of dollars to solve. I'm not going to turn you into a data scientist; no 2 hour, or even 40 hour online course is able to do that. But this course can teach you skills that you can use to add value and solve business problems from day 1.This course is different than most for several reasons:1. We start with problem solving instead of coding. I feel like starting to code before solving problems is misguided; many students are turned off by hours of work to try to write a couple of meaningless lines rather than solving real problems. The key value add data scientists make is solving problems, not writing something in a language a computer understands.2. The examples are based on real client work. This is not like other classes that use Kaggle data sets for who survived the Titanic, or guessing what type of flower it is based on petal measurements. Those are interesting, but not useful for people wanting to sell more products, or optimize the performance of their teams. These examples are based on real client problems that companies spent big money to hire consultants (me) to solve.3. Visual workflows. KNIME uses a visual workflow similar to what you'll see in Alteryx or Azure Machine Learning Studio and I genuinely think it is the future of data science. It is a better way of visualizing the problem as your are exploring data, cleaning data, and ultimately modeling. It is also something that makes your process far easier to explain to non-data scientists making it easier to work with other parts of your business.Summary: This course covers the full gamut of the machine learning workflow, from data and business u
En este curso veremos cómo implementar nuestros modelos de inteligencia artificial en una aplicación Android utilizando Tensorflow Lite. El tensorFlow lite es un conjunto de herramientas que nos ayuda a ejecutar modelos de TensorFlow en dispositivos móviles, integrados y de IoT. Esta nos permitirá realizar la inferencia en un dispositivo móvil. Implementaremos desde cero un modelo de “Regresión Lineal” en Python y lo llevaremos a Android utilizando Tensorflow Lite. Implementaremos desde cero un modelo de “Regresión en Múltiple” con normalización de datos y lo llevaremos a Android utilizando Tensorflow Lite. Implementaremos desde cero una “Red Neuronal Convolucional” para clasificar imágenes y llevaremos el modelo a Android utilizando Tensorflow Lite. Implementaremos un ejemplo de detección de objetos basado en la “Red Neuronal Convolucional” MobileNet. Implementaremos desde cero una “Red Neuronal Artificial” para clasificar dígitos utilizando el dataset MNIST y llevaremos el modelo a Android para reconocer dígitos del 0 al 9 utilizando Tensorflow Lite. Entrenamiento del algoritmo Yolo en Google Colab y despliegue en Aplicación Android. Veremos también como descargar cientos de imágenes para elaborar datasets de manera automática. Implementaremos la técnica de “Data Augmentation” para incrementar la precisión de nuestros modelos de clasificación de imágenes. Además implementaremos OpenCV para segmentar y reconocer digitos escritos a mano.Los invito cordialmente a tomar el curso en donde aprenderán a implementar sus modelos de inteligencia artificial en una aplicación Android.
One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.Why should you learn Data Cleaning?Improve decision makingImprove the efficiencyIncrease productivityRemove the errors and inconsistencies from the datasetIdentifying missing valuesRemove duplicationWhy should you take this course?Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It’s true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions whe
This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This is a comprehensive course with very crisp and straight forward intent. This course covers a variety of topics, including Neural Network BasicsTensorFlow detailed,Keras,Sonnet etcArtificial Neural NetworksTypes of Neural networkFeed forward networkRadial basis networkKohonen Self organizing mapsRecurrent neural NetworkModular Neural networksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksMachine Learning Deep Learning Framework comparisons There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the grap
¿Te suenan términos como *Machine Learning* o *Data Scientist*? ¿Te has preguntado para qué se utilizan estas técnicas y por qué las empresas están dispuestas a pagar entre 120.000 y 200.000 dólares al año a un científico de datos?Este curso está diseñado para resolver todas tus dudas y brindarte una formación integral en Data Science. Juan Gabriel Gomila, un profesional reconocido en el campo del Data Science, te guiará a lo largo del curso, compartiendo su vasto conocimiento y ayudándote a desmitificar la teoría matemática detrás de los algoritmos de Machine Learning. Aprenderás a dominar las librerías de Python que son esenciales en esta área, convirtiéndote en un experto en la materia.A lo largo del curso, abordarás conceptos y algoritmos clave del Machine Learning, de manera progresiva y detallada. Cada sección te proporcionará nuevas habilidades que te permitirán comprender y aplicar los principios del Data Science, una disciplina no solo fascinante, sino también altamente lucrativa.Además, este curso mantiene el estilo característico y ameno de Juan Gabriel Gomila, lo que hará que disfrutes aprendiendo técnicas de Machine Learning con Python.El curso incluye ejercicios prácticos y datasets basados en ejemplos del mundo real, lo que te permitirá no solo aprender la teoría, sino también aplicarla en la creación de tus propios modelos de Machine Learning. Además, tendrás acceso a un repositorio en GitHub con todo el código fuente en Python, listo para descargar y usar en tus proyectos.¡No esperes más! Únete a este curso y comienza a formarte en Machine Learning con el programa más completo y práctico del mercado en español.
Learn Python for Machine Learning & Data Science Masterclass
This track covers the fundamental concepts of machine learning, with a focus on supervised learning techniques using Python.
Hi all Its Jay I am a data scientist by profession and Instructor by passion I have around 4 years of experience as data scientist, I started my career as analyst as gradually moved to data scientist hence I can understand what are programming prerequisites for data scientist. This course is created for absolute beginners of data science and machine learning. It covers all aspect of python languages required in data science machine learning and deep learning.
** Mike's courses are popular with many of our clients." Josh Gordon, Developer Advocate, Google **"This is well developed with an appropriate level of animation and illustration." - Bruce"Very good course for somebody who already has pretty good foundation in machine learning." - Il-Hyung ChoWelcome to Hands-On Keras for Machine Learning Engineers. This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and how to use it to develop and evaluate deep learning models.There are two top numerical platforms for developing deep learning models, they are Theano developed by the University of Montreal and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super simple to use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models. My name is Mike West and I'm a machine learning engineer in the applied space. I've worked or consulted with over 50 companies and just finished a project with Microsoft. I've published over 50 courses and this is 55 on Udemy. If you're interested in learning what the real-world is really like then you're in good hands.Who is this course for? This course is for developers, machine learning engineers and data scientists that want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Basic concepts like cross-validation and one hot encoding used in lessons and projects are des
Este curso abrange uma jornada empolgante na aplicação do ChatGPT no campo da Ciência de Dados e Machine Learning. Ao longo deste programa, você explorará a capacidade do ChatGPT como uma ferramenta valiosa na análise de dados, no pré-processamento e na construção de modelos de aprendizado de máquina sem precisar digitar uma linha sequer de código!Na primeira parte, mergulharemos nas técnicas fundamentais de análise de dados. Você aprenderá como extrair informações estatísticas cruciais de seus conjuntos de dados, lidar com valores ausentes e identificar e tratar valores atípicos (outliers). Exploraremos as relações entre as variáveis e a representação visual de dados categóricos e numéricos. Além disso, você terá a oportunidade de criar gráficos interativos, tornando a exploração de dados mais envolvente e informativa. Na segunda parte, nos aprofundaremos no campo do machine learning e você aprenderá a lidar com atributos categóricos usando técnicas como o LabelEncoder e o OneHotEncoding. Abordaremos o desafio de conjuntos de dados desbalanceados e discutiremos a importância da transformação de escala. Você também ganhará experiência na divisão eficaz de bases de dados, seleção de algoritmos apropriados e métodos de avaliação. A validação cruzada, tuning de parâmetros e seleção de atributos são partes essenciais do processo de modelagem, e você terá a oportunidade de aprimorar suas habilidades nessas áreas.Ao concluir este curso, você estará equipado com habilidades avançadas em ciência de dados e machine learning, capacitado para aplicar o ChatGPT de forma eficaz em projetos do mundo real. Este programa oferece uma oportunidade única de melhorar suas habilidades analíticas e se destacar no campo da ciência de dados e do aprendizado de máquina. Prepare-se para alcançar um novo patamar em sua carreira profissional!
A book that provides a comprehensive guide to machine learning using two popular Python libraries, covering a wide range of supervised learning models.
DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS using R Programming, PYTHON Programming, WEKA Tool Kit and SQL. This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python Programming, WEKA tool kit and SQL.Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis. So we need a programming language which can cater to all these diverse needs of data science. R and Python shines bright as one such language as it has numerous libraries and built in features which makes it easy to tackle the needs of Data science.In this course we will cover these the various techniques used in data science using the R programming, Python Programming, WEKA tool kit and SQL.The most comprehensive Data Science course in the market, covering the complete Data Science life cycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, programming languages like R programming, Python are covered extensively as part of this Data Science training.
A LinkedIn Learning course that explores the intersection of causal inference and machine learning, teaching how to build more robust and interpretable models.
Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today Let’s parse that. The course is down-to-earth : it makes everything as simple as possible - but not simpler The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff. You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is. The course is very visual : most of the techniques are explained with the help of animations to help you understand better. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall. What's Covered: Machine Learning: Supervised/Unsu
¡Bienvenido al apasionante mundo de la Ciencia de Datos y Machine Learning en R! En este curso, te embarcarás en un viaje transformador para descubrir el poder de los datos y cómo convertirlos en conocimiento significativo. Aprenderás a dominar las herramientas y técnicas más avanzadas de R para analizar, visualizar y manipular datos caóticos. Además, desbloquearás el potencial de la inteligencia artificial al desarrollar modelos de aprendizaje automático capaces de predecir tendencias, clasificar información y comprender el lenguaje humano. ¡Prepárate para convertirte en un experto en la ciencia detrás de los datos y llevar tu capacidad analítica a un nivel completamente nuevo! ¿Listo para desafiar tus límites y cambiar el juego con la ciencia de datos y el aprendizaje automático en R? ¡Únete a nosotros y comienza tu emocionante aventura hacia el futuro de la tecnología y la innovación! Lo mas importante de este curso es que haremos un proyecto real para que puedas tener conocimientos adecuados y útiles en tu vida profesional. Cada que repliques este curso que realizaremos acá, iras aumentando tu probabilidad de tener {éxito en esta área. Es fundamental que tengas toda la disposición de retarte a entender este apasionante mundo. No olvides que cualquier duda puedes contactarme para que nada obstaculice tu aprendizaje
This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.Build confidence with guided, step-by-step demos, and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, we'll break down and explore machine learning techniques to help you understand exactly how and why they work.Follow along with simple, visual examples and interact with user-friendly, Excel-based models to learn topics like linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis, and more – without writing a SINGLE LINE of code.This course combines 4 best-selling courses from Maven Analytics into a single masterclass:PART 1: Univariate & Multivariate ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningPART 1: Univariate & Multivariate ProfilingIn Part 1 we’ll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We’ll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:Section 1: Machine Learning Intro & LandscapeMachine learning process, definition, and landscapeSection 2: Preliminary Data QAVariable types, empty values, range & count calculations, left/right
The fields of Artificial Intelligence and Machine Learning are considered the most relevant areas in Information Technology. They are responsible for using intelligent algorithms to build software and hardware that simulate human capabilities. The job market for Machine Learning is on the rise in various parts of the world, and the trend is for professionals in this field to be in even higher demand. In fact, some studies suggest that knowledge in this area will soon become a prerequisite for IT professionals.To guide you into this field, this course provides both theoretical and practical insights into the latest Artificial Intelligence techniques. This course is considered comprehensive because it covers everything from the basics to the most advanced techniques. By the end, you will have all the necessary tools to develop Artificial Intelligence solutions applicable to everyday business problems. The content is divided into seven parts: search algorithms, optimization algorithms, fuzzy logic, machine learning, neural networks and deep learning, natural language processing, and computer vision. You will learn the basic intuition of each of these topics and implement practical examples step by step. Below are some of the projects/topics that will be covered:Finding optimal routes on city maps using greedy search and A* (star) search algorithmsSelection of the cheapest airline tickets and profit maximization using the following algorithms: hill climb, simulated annealing, and genetic algorithmsPrediction of the tip you would give to a restaurant using fuzzy logicClassification using algorithms such as Naïve Bayes, decision trees, rules, k-NN, logistic regression, and neural networksPrediction of house prices using linear regressionClustering bank data using k-means algorithmGeneration of association rules with A
Welcome to the course wine quality prediction! In this course you will learn how to work with data from end-to-end and create a machine learning model that predicts the quality of wines.This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.).It is super important to notice that you will need python knowledge to be able to understand this course. You are going to develop everything using Google Colab, so there is no need to download Python or Anaconda. You also need basic knowledge of Machine Learning and data science, but don't worry we will cover the theory and the practical needs to understand how each of the models that we are going to use work.In our case, we will work with a classification problem (a set from the supervised learning algorithms). That means that we will use the quality as the target variable and the other variables as the inputs. In this sense, we will some examples to train our model and predict the quality of other wines.You will learn to work with Decision Trees, Logistic Regression, how to use LazyPredict and how to tune the hyperparameters using Grid Search.
Machine Learning Para Todos: Fundamentos Básicos de la IA¿Sientes curiosidad por la Inteligencia Artificial pero te parece un mundo complejo? Este curso te desmitifica el Machine Learning, brindándote una base sólida y accesible, ¡sin necesidad de experiencia previa en programación o matemáticas avanzadas!"Machine Learning Para Todos" está diseñado para cualquier persona con curiosidad por la IA y el deseo de comprender cómo funciona el aprendizaje automático. No se requieren conocimientos previos especializados; solo una mente abierta y ganas de aprender. Ya seas un profesional buscando nuevas habilidades, un estudiante explorando campos emergentes o simplemente alguien interesado en la tecnología del futuro, este curso te proporcionará una base sólida para comprender y aplicar los fundamentos del Machine Learning.A través de explicaciones claras, ejemplos prácticos y ejercicios sencillos, descubrirás los conceptos fundamentales detrás de la IA que está transformando nuestro mundo. Aprenderás qué es el Machine Learning, cómo funciona, los diferentes tipos de algoritmos (como regresión y clasificación), y cómo se aplican en situaciones reales, desde recomendaciones personalizadas hasta detección de fraudes.Al finalizar este curso, tendrás una comprensión clara de los conceptos fundamentales del Machine Learning, la capacidad de identificar problemas que pueden resolverse con estas técnicas y el conocimiento básico para seguir explorando este campo en temas como Deep Learning o Redes Neuronales Profundas, Inteligencia Artificial Generativa y Agentes IA. ¡Únete a nosotros y desbloquea el potencial de la Inteligencia Artificial!
A warm welcome to the Machine Learning and Data Science Interview Guide course by Cloud Excellence Academy.We provides this unique list of Data Science Interview Questions and Answers to help you prepare for the Data Scientist and Machine Learning Engineer interviews. This exhaustive list of important data science interview questions and answers might play a significant role in your interview preparation career and helping you get your next dream job. The course contains real questions with fully detailed explanations and solutions. Not only is the course designed for candidates to achieve a full understanding of possible interview questions, but also for recruiters to learn about what to look for in each question response. Why Data Science Job ?According to Glassdoor, a career as a Data Scientist is the best job in America! With an average base salary of over $120,000, not only do Data Scientists earn fantastic compensation, but they also get to work on some of the world's most interesting problems! Data Scientist positions are also rated as having some of the best work-life balances by Glassdoor. Companies are in dire need of filling out this unique role, and you can use this course to help you rock your Data Scientist Interview!Let's get started!Unlike others, We offer details explanation to each and every questions that will help you to understand the question100% money back guarantee (Unconditional, we assure that you will be satisfied with our services and be ready to face the data science interview).The Course highlights100 Questions on Machine Learning Algorithms , Use Cases ,Scenarios, Regularizations etc.75 Questions on Deep Learning ( ANN , CNN , RNN , LSTM , Transformer)100 Questions on Statistics and Probability 50 Question on Pyth
In this course, we will implement a neural network from scratch, without dedicated libraries. Although we will use the python programming language, at the end of this course, you will be able to implement a neural network in any programming language. We will see how neural networks work intuitively, and then mathematically. We will also see some important tricks, which allow stabilizing the training of neural networks (log-sum-exp trick), and to prevent the memory used during training from growing exponentially (jacobian-vector product). Without these tricks, most neural networks could not be trained. We will train our neural networks on real image classification and regression problems. To do so, we will implement different cost functions, as well as several activation functions. This course is aimed at developers who would like to implement a neural network from scratch as well as those who want to understand how a neural network works from A to Z. This course is taught using the Python programming language and requires basic programming skills. If you do not have the required background, I recommend that you brush up on your programming skills by taking a crash course in programming. It is also recommended that you have some knowledge of Algebra and Analysis to get the most out of this course. Concepts covered : Neural networks Implementing neural networks from scratch Gradient descent and Jacobian matrix The creation of Modules that can be nested in order to create a complex neural architecture The log-sum-exp trick Jacobian vector product Activation functions (ReLU, Softmax, LogSoftmax, ...) Cost functions (MSELoss, NLLLoss, ...) This course will be frequently updated, with the addition of bonuses. Don't wait any longer before launching yourself i
A área de Machine Learning (Aprendizagem de Máquina) e Data Science (Ciência de Dados) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando o Python, que é uma das linguagens de programação mais relevantes nesta área. Além disso, vamos utilizar o Google Colab para a implementação dos exemplos, o que facilita o entendimento dos conceitos e evita problemas de instalação de bibliotecas. Este curso é considerado de A à Z pelo fato de apresentar desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmo! O curso é dividido em cinco partes principais:Classificação - pré-processamento dos dados, naïve bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadoresRegressão - regressão linear simples e múltipla, polinomial, árvores de dec
Are you ready to start your journey into Python programming and machine learning? This course is your ultimate guide to becoming a skilled Python programmer and mastering machine learning from scratch. Whether you're a beginner or have some experience, this course will take you from zero to hero with a practical, hands-on approach.What You’ll Learn:Python Fundamentals: Master variables, data types, control flow, functions, and libraries.Data Preprocessing: Learn to clean, scale, and transform data for machine learning models.Machine Learning Basics: Build regression and classification models with real-world datasets.Advanced ML Techniques: Explore clustering, dimensionality reduction, and ensemble learning.Real-World Projects: Solve practical problems like predicting housing prices and customer segmentation.Why Take This Course?This course is designed for learners who want to gain practical programming and machine learning skills. You’ll work on real-world projects, gaining confidence to apply these skills in various industries. By the end of the course, you’ll have a strong portfolio and the ability to build your own machine learning models.Who This Course is For:Complete beginners looking to learn Python and machine learning.Professionals aiming to enhance their data science skills.Students and developers curious about applying machine learning in real-world scenarios.Join now to kickstart your career in data science and AI!
From Google Translate to Netflix recommendations, neural networks are increasingly being used in our everyday lives. One day neural networks may operate self driving cars or even reach the level of artificial consciousness. As the machine learning revolution grows, demand for machine learning engineers grows with it. Machine learning is a lucrative field to develop your career.In this course, I will teach you how to build a neural network from scratch in 77 lines of Python code. Unlike other courses, we won't be using machine learning libraries, which means you will gain a unique level of insight into how neural networks actually work. This course is designed for beginners. I don't use complex mathematics and I explain the Python code line by line, so the concepts are explained clearly and simply.This is the expanded and improved video version of my blog post "How to build a neural network in 9 lines of Python code" which has been read by over 500,0000 students.Enroll today to start building your neural network.
The Machine Learning Bootcamp for Complete Beginners 2025 is the fastest way to start your journey into Python programming, data science, and machine learning—no prior experience required.We’ll start from the very basics of Python: data types, variables, loops, functions, classes, exceptions, file handling, and test-driven development. You’ll also work with databases and APIs, which are essential for handling real-world data.Once you’re confident with Python, we’ll dive into the heart of machine learning. Step by step, you’ll explore and apply key algorithms:Linear Regression – predicting house and car pricesLogistic Regression – classifying health and customer dataDecision Trees & Random Forests – modeling complex decisionsKMeans Clustering – grouping unlabeled dataPCA (Principal Component Analysis) – reducing dimensions for big dataFinally, you’ll build and deploy a capstone project: a House Price Prediction web app with Flask, bringing everything you’ve learned into a practical, real-world project.This bootcamp focuses on hands-on coding, practical datasets, and real projects so that you’re not just learning theory—you’re building skills you can use right away.Who Is This Course For?This course is designed for:Absolute beginners with no prior coding experience.Students or professionals curious about AI and machine learning.Career changers looking to enter the data science or AI field.Developers who want to strengthen their Python and ML foundations.Anyone who wants to understand how machine learning powers modern apps.What You Will LearnBy the end of this b
Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you’ll tackle data-driven problems. The effective blend of Machine Learning with Python, scikit-learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model. This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, scikit-learn and TensorFlow. The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you’ll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You’ll build your own models from scratch. The second course, Machine Learning with Scikit-learn, covers effective learning algorithms to real-world problems using scikit-learn. You’ll build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. The third cou
Get instant access to a 69-page Machine Learning workbook containing all the reference materialOver 9 hours of clear and concise step-by-step instructions, practical lessons, and engagementIntroduce yourself to our community of students in this course and tell us your goalsEncouragement & celebration of your progress: 25%, 50%, 75%, and then 100% when you get your certificateWhat will you get from doing this course?This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyse raw real-time data, identify trends, and make predictions. You will explore key techniques and tools to build Machine Learning solutions for businesses. You don’t need to have any technical knowledge to learn these skills.What will you learn:What is Machine LearningSupervised Machine LearningUnsupervised Machine LearningSemi-Supervised Machine LearningTypes of Supervised Learning: ClassificationRegressionTypes of Unsupervised Learning: ClusteringAssociationData CollectionData PreparingSelection of a ModelData Training and EvaluationHPT in Machine LearningPrediction in MLDPP in MLNeed of DPPSteps in DPPPython LibrariesMissing, Encoding, and Splitting Data in MLPython, Java, R,and C ++How to install python and anaconda?Interface of Jupyter NotebookMathematics in PythonEuler's Number and VariablesDegree into Radians and Radians into Degrees in PythonPrinting Functions in PythonFeature Scaling for ML<p
This course covers a wide range of machine learning algorithms in R, including a dedicated section on tree-based methods.
You don’t want to code, but you do want to know about Big Data, Artificial Intelligence and Machine Learning? Then this course is for you!You do want to code and you do want to learn more about Machine Learning, but you don’t know how to start? Then this course is for you!The goal of this course is to get you as smoothly as possible into the World of Machine Learning. All the buzzwords will now be clear to you. No more confusion about “What’s the difference between Machine Learning and Artificial Intelligence.” No more stress about “This is just too much information. I don’t know where to start”The topics in this course will make it all clear to you. They are :Part 1 - WelcomePart 2 - Why machine learning?Part 3 - BuzzwordsPart 4 - The Machine Learning ProcessPart 5 - ConclusionBut it does not have to end here. As a bonus, this course includes references to the courses which I find the most interesting. As well as other resources to get you going.
This Johns Hopkins University specialization provides a comprehensive overview of the entire data science pipeline, including statistical modeling and machine learning algorithms.
This specialization from Google Cloud on Coursera teaches how to build and deploy ML models on Google Cloud Platform. It covers Vertex AI, AutoML, BigQuery ML, and TensorFlow, preparing learners for a career in cloud-based machine learning.
This specialization covers the essential mathematical foundations for machine learning, including linear algebra, multivariate calculus, and principal component analysis (PCA). It's designed to provide the necessary mathematical background for a career in ML.
This specialization from DeepLearning.AI provides a foundational understanding of the mathematics essential for AI and machine learning. It covers linear algebra, calculus, probability, and statistics, with a focus on their application in data science. Learners will gain skills in statistical hypothesis testing, Bayesian statistics, and exploratory data analysis.
You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right?You've found the right Machine Learning course!After completing this course you will be able to:· Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategy· Answer Machine Learning, Deep Learning, R, Python related interview questions· Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitionsCheck out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python.Why should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python.Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on it. And after running data analysis, you should be able to judge how good your m
En este curso se enseñan todos los conocimientos necesarios para convertirse en un Data Scientist (Científico de Datos). Para ello usaremos el lenguaje de Programación Python como herramienta, ya que es uno de los lenguajes con más demanda hoy en dia.En concreto, se tratarán en profundidad los siguientes apartados:- Programación en Python, donde aprendemos a programar en uno de los lenguajes más populares hoy en día como es Python.- Análisis de Datos, donde aprenderemos como realizar un Análisis Exploratorio de Datos, usando técnicas estadísticas y de Visualización de Datos.- Machine Learning, donde aprenderemos como crear modelos predictivos, evaluarlos y usarlos en un entorno de desarrollo.- Deep Learning, donde nos enfocamos en la creación de Redes Neuronales.- Web Scraping, donde aprenderemos técnicas para extraer información de páginas web.- Big Data, donde aprenderemos a como procesar datasets de gran tamaño asi como entrenar modelos predictivos con ellos.
In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:computer visionnatural language processingfinancial analysishealthcaregenomicsWhy should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You'll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!Thank you for reading and I hope to see you soon!Suggested Prerequisites:Decent Python programming skillComfortable with data science libraries like Numpy and MatplotlibFor the advanced section, probability knowledge is requiredWHAT ORDER SHOULD I TAKE YOUR COURSES IN?Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeLess than 24 hour
This course delves into advanced machine learning techniques, including an in-depth look at ensemble learning methods like bagging, boosting, and stacking.
This course focuses on the theory and practice of various classification algorithms in machine learning.
Explore supervised machine learning algorithms, prediction tasks, and model selection. Learn to improve performance using linear/logistic regression, KNN, decision trees, ensembling methods, and kernel techniques like SVM.
An intermediate-level course that introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. It covers basic statistics of data sets, such as mean values and variances and the computation of distances and angles between vectors using inner products.
This course includes a dedicated module on Data QA & Profiling. It covers techniques for univariate and multivariate profiling, common data quality issues like missing values, and data visualization for profiling.
This course explores supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes.
Learn to formulate and solve various optimization models that are central to machine learning algorithms.
A project-based course where you will learn to build and evaluate classification trees using Python.
This project-based course teaches how to train several classification algorithms like Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier to predict the churn rate of Telecommunication Customers.
A comprehensive article that explains the concepts of feature engineering and selection, and provides methods for handling missing data, continuous features, and categorical features, along with different feature selection techniques.
This specialization by Stanford University, taught by Andrew Ng, is a highly popular and comprehensive introduction to machine learning. It covers fundamental concepts including Support Vector Machines (SVMs) and kernel methods. The course is designed for beginners and provides a strong theoretical and practical foundation.
This is a 3-course specialization that provides a broad introduction to modern machine learning. It covers supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for AI and machine learning innovation.
An introductory course that covers the data science process, including data acquisition, cleaning, and transformation, using tools like R and Python.
This course from Microsoft on edX covers the essential mathematical foundations for machine learning and AI using Python.
Learn about various optimization algorithms and their applications in machine learning and data analysis.
This course provides a deep dive into the theory of supervised learning, and then applies this theory to practical problems using Python.
Welcome to this non-technical training for executives!This exclusive Udemy for Business training is designed to give you a high level overview of the key topics in Data Science and Machine Learning. Designed exclusively for students who want to learn about the basics of data science and machine learning at a high level, without needing to learn how to code or cover complex mathematics.In this course you'll learn the fundamentals to high quality data, allowing you to understand what makes data suitable for analysis and machine learning. Then we'll give you a quick overview of important statistical topics, such as mean, standard deviation, and the normal distribution. Afterwards you will learn the different ways data scientists are able to visualize data to convey their ideas in a clear manner.Once we've learned the basics of data, statistics, and visualization we'll explore the amazing opportunities machine learning has to offer. We'll teach you about the machine learning process, acquiring data, cleaning data, and an overview of the train/test split philosophy that supervised learning adheres to. Then we'll show you some examples of regression and classification algorithms, as well as how to evaluate their results.Once we understand regression and classification, we'll teach you about clustering techniques such as KMeans algorithm and dimensionality reduction methods like Principal Component Analysis.Let's being your first steps into data science and machine learning! Enroll today and we'll see you inside the course!
Der Kurs ist ein Einsteiger-Kurs in die Welt des Data Science, des Machine Learning, der künstlichen Intelligenz und dem Arbeiten mit Daten. In Zeiten der Digitalisierung und der digitalen Transformation stellt die Wissenschaft der Daten (Data Science) immer mehr eine zentrale Disziplin dar. Ohne grundlegende Kenntnisse und Qualifikationen im Bereich der Daten sind viele Arbeitsplätze kaum noch denkbar.Der Kurs liefert daher einen unkomplizierten Einstieg in die Welt der Daten und der Algorithmen. Dadurch ergibt sich ein Grundverständnis, was Daten überhaupt sind und man sie einer automatischen Verarbeitung mit Algorithmen zugänglich machen kann. Alle Algorithmen und mathematischen Verfahren werden Schritt für Schritt erklärt.Der Lernpfad dieses Kurses besteht u.a. aus folgenden Abschnitten:- Was sind Daten?- Datentypen, Data Mining und Visualisierung von Daten- Statistische Grundbegriffe- Einfache Clustering-Verfahren- Lineare und logistische Regression- Kurze Einführung in die Graphentheorie- Entscheidungsbäume und Random Forest- Einführung in die neuronalen Netze- Überblick über generative KI und deren AnwendungenAlle Algorithmen und Verfahren werden so ausführlich erläutert, dass keine speziellen mathematischen Vorkenntnisse oder IT-Fähigkeiten erforderlich sind. Ein grundlegendes Interesse an mathematischen Zusammenhängen wird hingegen vorausgesetzt. Die Beispiele stehen im Quellcode in der Programmiersprache Python zum Download und zum selber ausprobieren bereit.Der Kurs richtet sich insbesondere an Fach- und Führungskräfte, die selbst mit Daten arbeiten und sich ein tieferes Verständnis grundlegender Zusammenhänge erarbeiten möchten.
A área de Machine Learning (Aprendizagem de Máquina) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando a ferramenta Weka, que é uma das ferramentas mais utilizadas para machine learning e mineração de dados. Além disso, também utilizaremos a linguagem de programação Java para fazer a integração com o Weka! Este curso apresenta desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmos! O curso é dividido em cinco partes:Classificação - extração de características de imagens, naive bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadoresRegressão - regressão linear simples e múltipla, polinomial, árvores de decisão, random forest, vetores de suporte e redes neurais artificiaisRegras de associação - algoritmo aprio
A área de Machine Learning (Aprendizagem de Máquina) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando o R, que é uma das linguagens de programação mais relevantes nesta área de ciência de dados. Este curso é considerado de A à Z pelo fato de apresentar desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmos! O curso é dividido em cinco partes principais:Classificação - pré-processamento dos dados, naive bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadoresRegressão - regressão linear simples e múltipla, polinomial, árvores de decisão, random forest, vetores de suporte (SVR) e redes neurais artificiaisRegras de associação - algoritmos apriori e ECLATAgrupamento - k-means, agrupamento hierárqu
Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with
Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Calculus is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.Are you ready?Let's go!Suggested prerequisites:Firm understanding of high school math (functions, algebra, trigonometry)
With the increase of data by each passing day, Data Science has become one of the most important aspects in most of the fields. From healthcare to business, everywhere data is important. However, it revolves around 3 major aspects i.e. data, foundational concepts and programming languages for interpreting the data. This course teaches you everything about all the foundational mathematics for Data Science using R programming language, a language developed specifically for performing statistics, data analytics and graphical modules in a better way.Why Learn Foundational mathematical Concepts for Data Science Using R?Data Science has become an interdisciplinary field which deals with processes and systems used for extracting knowledge or making predictions from large amounts of data. Today, it has become an integral part of numerous fields resulting in the high demand of professionals of data science. From helping brands to understand their customers, solving complex IT problems, to its usability in almost every other field makes it very important for the functioning and growth of any organizations or companies. Depending upon the location the average salary of data scientist expert can be over $120,000. This course will help you learn the concepts the correct way.Why You Should Take This Online Tutorial?Despite the availability of several tutorials on data science, it is one of the online guides containing hand-picked topics on the concepts for foundational mathematics for Data Science using R programming language. It includes myriads of sections (over 9 hours of video content) lectured by Timothy Young, a veteran statistician and data scientists . It explains different concepts in one of the simplest form making the understanding of Foundational mathematics for Data Science very easy and effective.This Course includes:Overview of Machine Learning and R programming languageLinear Algebra- Scalars
In this training programme, you will learn Data Science and Machine Learning using Python & R. It will prepare students of any discipline to find lucrative jobs in the vast field of Data Science. Students will also learn Python and R in the process. Data Science is all about processing data received from various sources and deriving information and knowledge from that. This field uses statistics and machine learning tools. Applications are Market analysis, Predictive analytics, Demand Forecast, Recommender Systems, Social Media Analysis, Person analysis etc.
Deep learning would be part of every developer's toolbox in near future. It wouldn't just be tool for experts. In this course, we will develop our own deep learning framework in Python from zero to one whereas the mathematical backgrounds of neural networks and deep learning are mentioned concretely. Hands on programming approach would make concepts more understandable. So, you would not need to consume any high level deep learning framework anymore. Even though, python is used in the course, you can easily adapt the theory into any other programming language.
“We are bringing technology to philosophers and poets.”Machine Learning is usually considered to be the forte of professionals belonging to the programming and technology domain. People from arts and social science with no background in programming/technology often find it challenging to learn Machine Learning. However, Machine learning is not for technologists and programmers only. It is for everyone who wants to be a better researcher and decision-maker.Machine Learning is for anyone looking to model how humans and machines make decisions, develop mathematical models of decisions, improve decision-making accuracy based on data, and do science with data.Machine Learning brings you closer to the fascinating world of artificial intelligence. Machine Learning is a cross-disciplinary field encompassing computer science, mathematics, statistics, psychology, and management. It’s currently tough for normal learners to understand so many subjects, making Machine Learning inaccessible to many, especially those from social science backgrounds.We built this course, “Machine Learning for Social Scientists,” to help learners master this topic without getting stuck in its technicalities or fear of coding. This course is built as a scratch to the advanced level course for Machine Learning. All the topics are explained with the basics. The instructor creates a connection with everyday instances and fundamental tools so that learners feel connected to their previous learning. For example, we demo some Excel calculations to ensure learners can see the connection between Excel spreadsheet analysis and Machine Learning using R language.The course covers the following topics:· Fundamentals of Machine Learning· Applications of Machine Learning· Statistical concepts underlying Machine Learning· Supervised Machine Learning Algorithms· Unsupervised Machine Learning Algorithms· How to Use R to Implement Machi
Questo corso sul Data Science con R nasce per essere un percorso completo su come si è evoluta l'analisi dati negli ultimi anni a partire dall'algebra e dalla statistica classiche. L'obiettivo è accompagnare uno studente che ha qualche base di R in un percorso attraverso le varie anime del Data Science.Cominceremo con un ripasso delle basi di R, a partire dallo scaricamento e installazione, all'impostazione dell'ambiente di lavoro, passando per le strutture, la creazione di funzioni, l'uso degli operatori e di alcune funzioni importanti. Passeremo poi a vedere come manipolare e gestire un dataset, estrarne dei casi oppure delle variabili, generare dei dataset casuali, calcolare delle misure statistiche di base, creare grafici con i pacchetti Matplotlib e Seaborn.Nelle sezioni successive cominciamo a entrare nel cuore del Data Science con R, a cominciare dal preprocessing: vediamo infatti come ripulire e normalizzare un dataset, e come gestire i dati mancanti. La sezione successiva ci permette di cominciare a impostare dei modelli di machine learning con Python: vedremo tutti gli algoritmi più comuni, sia supervisionati che non supervisionati, come la regressione, semplice, multipla e logistica, il k-nearest neighbors, il Support Vector Machines, il Naive Bayes, gli alberi di decisione e il clustering. Passeremo poi ai più comuni metodi ensemble, come il Random Forest, il Bagging e il Boosting, e all'analisi del linguaggio naturale e al suo utilizzo nel machine learning per la catalogazione dei testi.Nelle ultime sezioni vedremo alcuni rudimenti di analisi temporale, sistemi di raccomandazione e social media mining.
¿Por qué estudiar ciencia de datos?Las empresas tienen un problema: recolectan y guardan enormes cantidades de datos en su día a día. El problema es que no tienen las herramientas y capacidades para extraer conocimiento y tomar decisiones a partir de esos datos. Pero eso está cambiando. Desde hace algunos años, la demanda de científicos de datos ha crecido exponencialmente. Tanto es así, que el número de personas con estas habilidades no es suficiente para cubrir todas las vacantes que hay. Una búsqueda básica en Glassdoor o Indeed te revelará por qué los salarios de los científicos de datos han crecido tanto en los últimos años.¿Por qué este curso?Casi todos los cursos que existen son demasiado teóricos o demasiado prácticos. Los cursos de universidad no suelen desarrollar las habilidades necesarias para enfrentarse a problemas de ciencia de datos desde cero, ni te enseñan a utilizar el software necesario de forma fluida. Por otra parte, muchos cursos y bootcamps online enseñan a utilizar estas técnicas sin llegar a entenderlas en profundidad, pasando por la teoría de forma superficial.Nuestro curso combina lo mejor de cada método. Por una parte, veremos de dónde surgen y por qué se utilizan estos métodos, entendiendo por qué funcionan de la forma que lo hacen. Por otra, vamos a programar estos métodos desde cero, utilizando las librerías más populares de la ciencia de datos y el machine learning en Python. Solo cuando hayas entendido exactamente cómo funciona cada algoritmo, aprenderemos a usarlos con las librerías avanzadas de Python.Contenido del cursoIntroducción al machine learning y a la ciencia de datos.Regresión lineal simple. Aprenderemos a estudiar la relación entre distintos fenómenos.Regresión lineal multiple. Crearemos modelos de más de una variable para estudiar el comportamiento de una variable de interés.Regresión
"Python TOTAL", el curso Best-Seller que ha enseñado Python desde cero a miles y miles, necesitaba un complemento perfecto: "Python TOTAL para Data Science y Machine Learning".¿Por qué hacía falta?Porque con este curso, además de aprender Python desde cero, podrás llevarlo hacia la ciencia del momento: Data Science (o Ciencias de la Información), para poder programar herramientas capaces de procesar cantidades monumentales de información, y de generar no solo visualizaciones relevantes, informativas y atractivas, sino también predicciones a partir de los datos que disponemos.Con "Python Total para Data science & Machine Learning" podrás ayudar a quienes toman decisiones a entender mejor el contexto y la realidad sobre la cual están operando, para poder ser eficaces, eficientes y acertivos en sus decisiones.¿Que encontrarás en este curso?18 días de aprendizaje intenso y prácticoCientos de ejercicios de código en la plataforma (3 por cada lección)Vientos de archivos de código descargableProyectos díarios del mundo real para aplicar lo aprendidoDecenas de bases de datos para prácticasCuestionariosLecciones teóricas y prácticas hechas con amor por la simplicidad¿Qué temas cubre este curso?Python básicoPandasNumPyMatplotlibSeabornScikit LearnTensorflowMachine LearningExcel y Power BI para Data ScienceAlgoritmos de Aprendizaje Supervisado, No Supervisado y por ReforzamientoBases de DatosAPIsDeep LearningEtica y Provacidad en Data Sciencey muchísimo más<
Fai un passo verso il futuro: AI, Machine Learning e Data Science.Sai cosa accomuna il successo dei più grandi colossi del web come Google, Amazon e Facebook ? L'utilizzo che hanno fatto del machine learning.Il machine learning è la branca dell'intelligenza artificiale che ha lo scopo di insegnare ai computer ad apprendere autonomamente, senza essere esplicitamente programmati.Il machine learning non è una novità, ma è finito sotto la luce dei riflettori solo con il nuovo millennio, per due motivi:L'enorme quantità di dati oggi disponibile sul web.Il progresso della tecnologia e il crescente aumento della potenza di calcolo.Questi due fattori, uniti alle sue innumerevoli applicazioni commerciali, stanno contribuendo alla crescita vertiginosa del machine learning che sta trascinando con se l'intero campo dell'intelligenza artificiale.In questo corso pratico imparerai come funziona il machine learning e come utilizzarlo in maniera pratica, utilizzando il linguaggio Python e librerie popolari come Scikit-learn, Pandas e PyPlot.Vuoi dare una svolta alla tua carriera ?L'esperto di machine learning è la professione del futuro e Linkedin lo conferma; secondo una loro recente ricerca il Machine Learning Engineer è la nuova figura più ricercata dalle aziende con un tasso di crescita di quasi il 1000% negli ultimi 5 anni ed è subito seguito dal Data Scientist.Al termine di questo corso avrai acquisito l'esperienza pratica e le intuizioni teoriche necessarie per lanciare la tua carriera in entrambe queste due nuove professioni.Vuoi fondare la tua startup nel campo dell'AI ?Il valore totale del mercato dell'intelligenza artificiale nel 2016 era di 1.3 miliardi di dollari; secondo una ricerca di un'importante società di analisi americana il suo valore per il 2025 potrebbe superare il 60 miliard
Hello there,Welcome to Python Numpy: Machine Learning & Data Science CoursePython numpy, Numpy python, python numpy: machine learning & data science, python numpy, machine learning data science course, machine learning python, data science, python, oak academy, machine learning, python machine learning, python data science, numpy course, data science courseLearn Numpy and get comfortable with Python Numpy in order to start into Data Science and Machine Learning OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you Data science is everywhere Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets Essentially, data science is the key to getting ahead in a competitive global climate Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small and the standard library is also large In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasksAre you re
Selamat datang di program Pelatihan Data Science dan Machine Learning Dengan R!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan R.Bagi rekan - rekan yang belum menguasai pemrograman R, pelatihan juga memberikan konten pemrograman dasar untuk Rsehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman R, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanakan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihanPemrograman RPengenalan tool dan editor seperti RStudio, Jupyter Notebook / JupyterLab, Jupyter / Notebook Dengan Anaconda, dan Google ColabVisualisasi DataVisualisasi Data dengan ggplot2Dataset, Pra-Proses dan Pengurangan Dimensi FeatureManipulasi dan Analisa dataEksplorasi data science dan machine learningPermasalahan dan Penyelesaian Kasus Linear RegressionPermasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Ensemble MethodsHyperparameter Tuning Untuk Model Machine LearningKumpulan Studi KasusJika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan ikut terlibat diskusinya.
Python est reconnu comme l'un des meilleurs langages de programmation pour sa flexibilité. Il fonctionne dans presque tous les domaines, du développement Web au développement d'applications financières. Cependant, ce n'est un secret pour personne que la meilleure application de Python est dans les tâches de data science, d'analyse de données et de Machine Learning.Bien que Python facilite l'utilisation du Machine Learning et de l'analyse de données, il sera toujours assez frustrant pour quelqu'un qui n'a aucune connaissance du fonctionnement de l'apprentissage automatique.Si vous avez envie d'apprendre l'analyse de données et le Machine Learning avec Python, ce cours est fait pour vous. Ce cours vous aidera à apprendre à créer des programmes qui acceptent la saisie de données et automatisent l'extraction de fonctionnalités, simplifiant ainsi les tâches du monde réel pour les humains.Il existe des centaines de ressources d'apprentissage automatique disponibles sur Internet. Cependant, vous risquez d'apprendre des leçons inutiles si vous ne filtrez pas ce que vous apprenez. Lors de la création de ce cours, nous avons tout filtré pour isoler les bases essentielles dont vous aurez besoin dans votre parcours d'apprentissage en profondeur.C'est un cours de base qui convient aussi bien aux débutants qu'aux experts. Si vous êtes à la recherche d'un cours qui commence par les bases et passe aux sujets avancés, c'est le meilleur cours pour vous.Il enseigne uniquement ce dont vous avez besoin pour vous lancer dans l'apprentissage automatique et l'analyse de données sans fioritures. Bien que cela aide à garder le cours assez concis, il s'agit de tout ce dont vous avez besoin pour commencer avec le sujet.
Cette formation est conçue pour donner une compréhension complète de la data science, avec un focus particulier sur l’utilisation du langage R, un des outils les plus performants pour l’analyse statistique et la visualisation de données. Voici ce que vous apprendrez au cours de cette formation :Les bases de R et de la data science : Nous commencerons par les bases du langage R, afin que vous puissiez maîtriser les outils essentiels de manipulation et d’analyse de données.Visualisation des données : L’une des compétences les plus recherchées aujourd’hui est la capacité à visualiser des données de manière claire et percutante. Nous apprendrons ensemble à utiliser des bibliothèques comme ggplot2 pour créer des graphiques informatifs.Nettoyage et préparation des données : Une partie essentielle de l’analyse de données consiste à préparer les jeux de données. Vous apprendrez à manipuler, transformer et nettoyer des données brutes pour les rendre exploitables.Algorithmes de machine learning : algorithmes d'apprentissage supervisé et non supervisé en montrant comment créer des modèles prédictifs pour résoudre des problèmes réels.Applications concrètes et projets : Tout au long de la formation, vous aurez l’occasion de travailler sur des exemples et des études de cas, afin de renforcer vos compétences et de vous préparer à intégrer le monde professionnel de la data science.
Fundamentos da linguagem de programação Python , que é a principal base de linguagem para a aplicação da ciência de dadosEstudo das principais funcionalidades da biblioteca Pandas , que é a principal biblioteca de manipulação de dados da Data ScienceEstudo das principais funcionalidades da biblioteca Numpy , que é a principal biblioteca de manipulação de operações matemáticasEstudo das principais bibliotecas de Visualização de Dados : Matplotlib e SeabornManipulando TimeSeries, que são os tipos usados em datas e horasRedução de Dimensões com PCA e TSNEEstatística para Data Science.Machine Learning , com teoria e aplicação prática de estratégias básicas e avançadasIntuição e aplicação dos seguintes modelos preditivos:Linear_Regression (Regressão Linear) Logistic_Regression (Regressão Lógica)Decision_Tree (Árvore de Decisão)Random_Forest (Floresta Aleatória)Stochastic_Gradient_Descent (SGD)Support_Vector_Machine (SVM) AdaBoostGradient_Boost (Impulsionamento Gradiente)K-Means_Clustering - (K-Médias de Grupos)K-Nearest_Neighbors (KNN) PROJETO: Predição da Idade dos Passageiros do Titanic (Regressão Linear)PROJETO: Classificação de sobrevivência dos passageiros do Titanic (Classificação)PROJETO: Análise de Sentimentos de Frases do Twitter (Processamento de Linguagem Natural - PLN)PROJETO: Funcionamento e uso do modelo de detecção e classificação de objetos em imagens e vídeos YOLO (Visão Computacional)PROJETO: Segregando Clientes por Padrões de
Why study data science?Companies have a problem: they collect and store huge amounts of data on a daily basis. The problem is that they don't have the tools and capabilities to extract knowledge and make decisions from that data. But that is changing. For some years now, the demand for data scientists has grown exponentially. So much so, that the number of people with these skills is not enough to fill all the job openings. A basic search on Glassdoor or Indeed will reveal to you why data scientist salaries have grown so much in recent years.Why this course?Almost every course out there is either too theoretical or too practical. University courses don't usually develop the skills needed to tackle data science problems from scratch, nor do they teach you how to use the necessary software fluently. On the other hand, many online courses and bootcamps teach you how to use these techniques without getting a deep understanding of them, going through the theory superficially.Our course combines the best of each method. On the one hand, we will look at where these methods come from and why they are used, understanding why they work the way they do. On the other, we will program these methods from scratch, using the most popular data science and machine learning libraries in Python. Only when you have understood exactly how each algorithm works, we will learn how to use them with advanced Python libraries.Course contentIntroduction to machine learning and data science.Simple linear regression. We will learn how to study the relationship between different phenomena.Multiple linear regression. We will create models with more than one variable to study the behavior of a variable of interest.Lasso regression. Advanced version of multiple linear regression with the ability to filter the most useful variables.Ridge regression. A
¡Hola a todos y bienvenidos a este curso sobre los fundamentos del Machine Learning y su aplicación en la solución de problemas reales mediante el uso de Python 3! Mi nombre es Santiago Hernández y voy a ser vuestro instructor a lo largo de este programa formativo, tenéis más información sobre mí en la biografía o en el vídeo “Presentación del instructor”.A lo largo de este curso sobre Machine Learning y Data Science presentaré, desde un nivel muy básico y al alcance de todo tipo de perfiles, los fundamentos teóricos y matemáticos que se necesitan para comprender en detalle el funcionamiento de los algoritmos de aprendizaje automático y las técnicas de ciencia de datos más importantes en la actualidad. Para ello, utilizaré el enfoque que mejores resultados me ha proporcionado al impartir este tipo de clases en diferentes universidades, un enfoque práctico, en el que veréis como se desarrollan las diferentes funciones y ecuaciones matemáticas de mi puño y letra. Representaré gráficamente todas las intuiciones matemáticas en las que se fundamenta el Machine Learning, de manera que, cualquier persona pueda comprenderlas y avanzar con las siguientes secciones. Este no es un curso para matemáticos, es un curso para todos aquellos que quieren adentrarse en el dominio del aprendizaje automático aprendiendo unas bases sólidas que le permitan solucionar problemas reales mediante la implementación en Python 3 de las principales técnicas existentes y comprender aquellos algoritmos que surjan en el futuro.A medida que vayamos construyendo y comprendiendo estos fundamentos teóricos, iremos aplicándolos a casos de uso prácticos en los que utilizaremos conjuntos de datos reales. Yo soy un firme creyente de que aquellas cosas que se aprenden de manera teórica deben saberse aplicar a casos de uso prácticos para sacarles todo el rendimiento posible, y
Datascience; machine learning, data science, python, statistics, statistics, r, machine learning python, deep learning, python programming, djangoHello there,Welcome to “ Kaggle - Get Best Profile in Data Science & Machine Learning ” course.Kaggle is Machine Learning & Data Science community. Boost your CV in Data Science, Machine Learning, Python with KaggleKaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, Oak Academy has a course to help you apply machine learning to your work. It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models.Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community-published data & code.Kaggle is
Werde zum gefragten Data-Science-Spezialisten mit R!Data-Science-Experten sind nicht nur gefragt wie nie, sie bekommen auch ein überdurchschnittliches Gehalt (laut Indeed Jobbörse). Diesen Kurs habe ich entwickelt, um dir den bestmöglichen Einstieg zu bieten.R ist eine unglaublich mächtige und effiziente Sprache, sowohl ob für Data Science als auch Machine Learning. Leider ist der Einstieg allerdings oft sehr trocken - nicht aber in diesem Kurs, alle Themen lernst du Schritt für Schritt und am Beispiel.=> "Wie auch bei Jannis' anderen Kursen ist alles top! Gute step by step Introduction." (★★★★★, Markus Dunkel)Besonders viele Übungen + Beispiele:In diesem Kurs werden alle Themen anschaulich erklärt - du analysierst Geburtsstatistiken & echte Gehälter aus San Francisco, erstellst ein Modell für Diabetes, extrahierst Raketenstarts aus einer Webseite (Web-Crawling) oder visualisierst in einer Grafik die Ausbreitung von Ebola bzw. dem Coronavirus. Schritt für Schritt lernst du also alles was du zum Thema R wissen musst - und zwar nicht nur die Sprache selbst, sondern auch alle wichtigen Tools drumherum, und wie R angewandt wird. Dadurch kannst du das Wissen aus dem Kurs sofort anwenden. Mit über 200+ HD-Videos und mehr als 23 Stunden Videomaterial ist dies der umfangreichste Data-Science Kurs mit R auf Udemy.Was lernst du alles?R Grundlagen:RStudio (unsere Entwicklungsumgebung)FunktionenVariablen,...Data Science:Lese Daten einErstelle anschauliche VisualisierungenÜberzeuge deine Kollegen durch überzeugende PDF-ReportsDiverse Beispiele!Machine Learning mit caret:Regre
This is an ambitious course. The goal here is simple: Only teach what you need to know for day 1 of your first data science job. No fluff, nothing out of context, no topics that are not relevant to real world applications. We will cover EVERY core topic and tool required for those new to data science: Python, R, SQL, Useful Math/Stats/Algorithms, Tableau, and Excel in depth. The course will cover skills that align with three different job types:- Data Analyst- General Data Scientist- Machine Learning EngineerYou can expect to learn from first principles the foundational topics and tools used in practice today. We will avoid topics that are not useful or are simply too advanced when starting out. Your journey will be guided by the Data Science Road Map, a collection of the best resources gathered through years of experience by the instructor.In addition, we will survey every important technology required on the job including GitHub, Kaggle, the basics of cloud, web development and docker. With over 200 videos, readings, and assignments, you can be sure you will be well prepared to join the data community.If you are just getting started or want to fill in some of your knowledge gaps this course is for you!
Linear Algebra is one of the essential foundations for anyone who wants to work in Data Science and Artificial Intelligence. Whether manipulating large datasets, building predictive models, or implementing Machine Learning algorithms, a solid understanding of this mathematical field is indispensable. This course is designed to provide an intuitive and practical approach to the most important concepts, combining theory and Python implementations to ensure you learn by applying. The course is divided into six sections, each covering a fundamental aspect of Linear Algebra. We begin with an introduction to core concepts, explaining the importance of this discipline and how it connects to Data Science and Machine Learning. Here, we cover elements like scalars, vectors, matrices, and tensors, along with setting up the necessary Python libraries. We also explore data representation and how linear systems are used to solve mathematical problems. In the second section, we dive deeper into vectors—their properties and applications. Vectors are fundamental components in data manipulation, feature scaling, and even defining the multidimensional spaces used in predictive models. You’ll learn about norms, unit vectors, orthogonal and orthonormal vectors, and visualize these structures intuitively through graphs. Next, we explore matrices, which are widely used to represent data and process large volumes of information. We’ll cover key matrix properties, norms, transposition, inversion, and essential decompositions for diverse applications. These concepts are critical for neural networks, linear regressions, and dimensionality reduction techniques. The fourth section focuses on operations involving vectors and matrices. We’ll study matrix multiplication, dot and cross products, reduction operations, and the cosine rule—essential tools for calculating data similarity and efficiently manipulating mathematical structures. Then, we tackle linear tr
Disclaimer:The second of this course demonstrates techniques using Jupyter Notebooks from Anaconda. You are welcome to follow along (however), it is not required to do these exercises to complete this course. If you are a Udemy Business user, please check with your employer before downloading software.Welcome!: Thank you all for the huge response to this emerging course! We are delighted to have over 20,000 students in over 160 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered. Most importantly: To make this course "real", we've expanded. In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections! We hope you enjoy the new content! Unlock the secrets of understanding Machine Learning for Data Science!In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come. Our exotic journey will include the core concepts of:The train wreck definition of computer science and one that will actually instead make sense. An explanation of data that will have you seeing data everywhere t
A Álgebra Linear é um dos fundamentos essenciais para quem deseja atuar com Ciência de Dados e Inteligência Artificial. Seja na manipulação de grandes conjuntos de dados, na construção de modelos preditivos ou na implementação de algoritmos de Machine Learning, a compreensão dessa área matemática é indispensável. Este curso foi estruturado para oferecer uma abordagem intuitiva e prática dos conceitos mais importantes, combinando teoria e implementações em Python para garantir que você aprenda aplicando.O curso é dividido em seis seções, cada uma abordando um aspecto fundamental da Álgebra Linear. Começamos com uma introdução aos conceitos básicos, onde explicamos a importância dessa disciplina e como ela se conecta com Data Science e Machine Learning. Aqui, são apresentados elementos como escalares, vetores, matrizes e tensores, além da instalação das bibliotecas necessárias para a programação em Python. Também exploramos a representação de dados e como os sistemas lineares são utilizados para resolver problemas matemáticos.Na segunda seção, aprofundamos o estudo dos vetores, suas propriedades e aplicações. Vetores são componentes fundamentais na manipulação de dados, na normalização de variáveis e até mesmo na definição de espaços multidimensionais usados em modelos preditivos. Você aprenderá sobre normas, vetores unitários, vetores ortogonais e ortonormais, além de visualizar essas estruturas de maneira intuitiva através de gráficos.Em seguida, exploramos as matrizes, que são amplamente utilizadas na representação de dados e no processamento de grandes volumes de informações. Conheceremos as principais propriedades das matrizes, suas normas, transposição, inversão e decomposições fundamentais para diversas aplicações. Esses conceitos são indispensáveis para o funcionamento de redes neurais, regressões lineares e técnicas de redução de dimensionalidade.A quarta seção é dedicada às operações envolvendo vetores e matrizes</st
Dans ce cours accéléré, nous allons aborder les opportunités qu'offrent les modèles génératifs et ensuite, nous nous intéresserons plus particulièrement aux Generative Adversarial Networks (GANs). Je vais vous expliquer le fonctionnement des GANs de manière intuitive et ensuite, nous nous plongerons dans l'article qui les a introduit en 2014 (Ian J. Goodfellow et al.). Je vous expliquerai donc de manière mathématique le fonctionnement des GANs, ce qui vous permettra d'avoir les bases nécessaires pour implémenter votre premier GAN en partant de zéro.Nous implémenterons en approximativement 100 lignes de code un générateur, un discriminateur et le pseudo-code décrit dans l'article afin d'entraîner ces derniers. Nous utiliserons le langage de programmation Python et le framework PyTorch. Après entraînement, le générateur nous permettra de générer des images synthétiques.J'ai la conviction qu'un concept s'apprend par la pratique et ce cours accéléré a pour objectif de vous donner les bases nécessaires afin de continuer votre apprentissage du Machine Learning, de PyTorch et des modèles génératifs (GANS, Variational Autoencoders, Normalizing Flows, ...).À l'issue de ce cours, le participant aura la possibilité d'utiliser Python (et plus particulièrement le framework PyTorch) afin d'implémenter des articles scientifiques et des solutions d'intelligence artificielle. Ce cours a également pour objectif d'être un tremplin dans votre apprentissage des modèles génératifs.Au-delà des GANs, ce cours est également une introduction générale au framework PyTorch et un cours de Machine learning de niveau intermédiaire .Concepts abordés:Le framework PyTorch afin d'implémenter et d'optimiser des réseaux de neurones.Le framework Keras afin de charger un ensemble de données.Google colab.L'utilisation des modèles génératifs dans le monde de la recherche et industri
A comprehensive course that teaches machine learning concepts through visual explanations and hands-on exercises. Perfect for beginners wanting to understand ML fundamentals.
Prepare for machine learning interviews with real-world ML system design problems and interview strategies.
A practical introduction to ML for developers. Learn to implement ML algorithms and integrate them into software applications.
Complete learning path covering Python, ML fundamentals, deep learning, and practical ML engineering skills.
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science. The course covers the basics of linear regression and its application in real-world scenarios.
This edX course focuses on the fundamentals of supervised machine learning, including both classification and regression. You will learn to apply various algorithms to real-life problems using Python and scikit-learn. The curriculum covers classification techniques and important concepts for evaluating and tuning your models.
Part of the Data Science Professional Certificate, this course covers popular machine learning algorithms, principal component analysis, and regularization. You will build a movie recommendation system.
This course helps you learn essential foundational math concepts for AI and machine learning, like calculus, linear algebra, and statistics, using a hands-on approach with Python.
This course from Microsoft introduces the fundamental principles of machine learning using Python. You will learn about various machine learning algorithms, including regression, and how to implement them.
This course from the University of Toronto provides a hands-on introduction to quantum-enhanced machine learning. It covers the intersection of quantum computing and machine learning, focusing on algorithms that are challenging for classical computers. The course emphasizes implementing protocols using open-source Python frameworks and features guest lectures from prominent researchers in the field.
This three-course professional certificate program, offered by Harvard University and Google TensorFlow, provides a deep dive into the emerging field of TinyML. It covers the essential language of TinyML, its real-world applications, and the practical deployment of machine learning models on resource-constrained embedded systems. The program emphasizes hands-on experience using a kit that includes an Arduino board.
A search result page on edX for courses related to optimization for machine learning, featuring courses from various universities.
A hands-on course that covers various aspects of feature engineering for both categorical and continuous variables, as well as text data.
Hello there,Machine learning python, python, machine learning, Django, ethical hacking, python bootcamp, data analysis, machine learning python, python for beginners, data science, machine learning, django:Welcome to the “Python: Machine Learning, Deep Learning, Pandas, Matplotlib” course. Python, Machine Learning, Deep Learning, Pandas, Seaborn, Matplotlib, Geoplotlib, NumPy, Data Analysis, TensorflowPython instructors on Udemy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work.In this course, we will learn what is Deep Learning and how does it work.This course has suitable for everybody who is interested in Machine Learning and Deep Learning concepts in Data Science.First of all, in this course, we will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After then we take a little trip to Machine Learning Python history. Then we will arrive at our next stop. Machine Learning in Python Programming. Here we learn the machine learning concepts, machine learning a-z workflow, models and algorithms, and what is neural network concept. After then we arrive at our next stop. Artificial Neural network. And now our journey becomes an adventure. In this adventure we'll enter the Keras world then we exit the Tensorflow world. Then we'll try to under
This course introduces you to regression analysis using the statsmodels library in Python. You'll learn how to build, interpret, and evaluate linear regression models.
This course teaches you how to apply machine learning techniques to time series data. It covers feature engineering, spectrograms, and advanced techniques for classification and prediction tasks.
This course provides a thorough introduction to the caret package in R for building and evaluating supervised learning models.
This course provides a comprehensive introduction to the scikit-learn library, the most popular Python library for machine learning. You'll learn how to use scikit-learn for a variety of machine learning tasks, including regression.
This course teaches you how to use tree-based models and ensembles for classification and regression in R.
This course provides a deep dive into regression analysis using Python. You will learn about simple and multiple linear regression, as well as techniques for model evaluation and selection.
This course focuses on regularization techniques, such as Ridge and Lasso regression, which are used to prevent overfitting in machine learning models. You will learn the theory behind these techniques and how to apply them in practice.
Learn to generate, explore, and evaluate machine learning models in R using the Tidyverse. The course covers multiple and logistic regression, tree-based models, and support vector machines.
This course covers four of the most common classification algorithms in R: k-nearest neighbors, logistic regression, Naive Bayes, and decision trees.
This course teaches you how to build predictive models using scikit-learn. You'll learn about classification and regression and apply your skills to real-world datasets.
A DataCamp project that dives into India's telecom sector to analyze customer churn. You'll use pandas and machine learning to study datasets from top telecom firms.
A career track on DataCamp that provides a comprehensive curriculum for aspiring machine learning scientists. It covers a wide range of topics, including supervised and unsupervised learning, deep learning, and natural language processing, with a focus on practical coding exercises.
Explore how to make powerful, accurate predictions with ensemble learners, one of the most common classes of machine learning algorithms.
This course shows you how to apply supervised learning techniques to real-world problems, focusing on both classification and regression tasks. You'll start with basic models and advance to more complex algorithms like decision trees and XGBoost.
This course provides a foundational understanding of linear regression, one of the most important algorithms in machine learning and AI. It covers the theory and practical implementation of linear regression.
Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and gain insights into causal inference from observational studies.
This course provides a practical introduction to machine learning using Python. It covers the entire machine learning workflow, from data preparation and feature engineering to model building and evaluation.
This intermediate-level course explains how to create one of the most common types of machine learning: supervised learning models.
A collection of courses on LinkedIn Learning focused on optimization techniques for machine learning and data science.
This course introduces learners to Vertex AI as a unified platform for building, training, and deploying AutoML machine learning models. It discusses the five phases of converting a use case to be driven by machine learning, emphasizing the importance of each step.
A learning path on Pluralsight that teaches how to apply common feature engineering techniques as part of the machine learning workflow specifically within the Microsoft Azure platform.
This course covers key mathematical concepts for machine learning and AI, with a focus on implementation using R.
This guide explains how to integrate machine learning capabilities directly into iOS applications using Apple's Core ML framework for on-device inference.
This free course teaches you the fundamentals of linear regression and its implementation in Python. It is a beginner-friendly course that covers the theory and practical aspects of this important machine learning algorithm.
The original Stanford ML course taught by Andrew Ng
Learn Duke University Introduction to Machine Learning
Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,... With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and HuggingfaceYou will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine Learning is the most in-demand and Highest Paying job of 2017 and the same trend will follow for the coming years. With an average salary of $120,000 (Glassdoor and Indeed), Machine Learning will help you to get one of the top-paying jobs. This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! At the end of the course you will be able to Master Machine Learning using PythonDemystifying Artificial Intelligence, Machine Learning, Data ScienceExplore & Define a ML use caseML Business Solution BlueprintExplore Spyder, Pandas and NumPyImplement Data EngineeringExploratory Data Analysis Introduction to Statistics and Probability DistributionsLearn Machine Learning MethodologyUnderstand Supervised Learning Supervised LearningImplement Simple & Multiple Linear RegressionDecision TreesRegression & Classification Model EvaluationCross Validation, Hyperparameter Ensemble ModelingRandom Forest & XGBoost Learning Machine Learning is a definite way to advance your career and will open doors to new Job opportunities. 100% MONEY-BACK GUARANTEE This course comes with a 30-day money back guarantee. If you're not happy, ask for a refund, all your money back, no questions asked. Feel forward to have a look at course description and demo videos and we look forward to see you inside.
The "Machine Learning and Data Science Diploma using Python" is a unique program that enriches Arabic content in the field of artificial intelligence. It's a comprehensive training course centered on interaction, practical application, thorough explanation, and detailed algorithms starting from scratch. The course ensures a robust understanding of algorithms leading to practical implementation, aiding in building strong models applicable to real-life scenarios. It caters to beginners and anyone intrigued by data science, its analysis, and the study of machine learning and artificial intelligence, including Data Analysts, Data Scientists, Machine Learning Engineers, and AI EngineersThis diploma not only equips you with the proficiency to learn machine learning and data science through coding but also ensures a solid grasp of the mathematics behind the algorithms. This understanding is essential for fine-tuning algorithmic parameters effectively.Topics covered in this diploma include:Definition of DiplomaLinear Algebra for Machine LearningData Exploration and PreparationProbability and Statistics for Data ScienceNumPy LibraryPandas LibraryVisualization Libraries (matplotlib, seaborn)Introduction to Machine Learning ConceptsNumerical OptimizationRegression with Different MethodsEnd-to-End Machine Learning ProjectsRegularizationKaggle PlatformClassification (Binary, Multiclass, different metrics)K-Nearest NeighborsNaive BayesLogistic RegressionSupport Vector MachinesDecision TreesEnsemble Learning (Voting, Bagging, Boosting)Hyperparameters TuningPractical ProjectsWhat C
Immerse yourself in the cutting-edge world of deep learning with TensorFlow through this comprehensive masterclass. Starting with an insightful overview and the scenario of perceptron, progress to creating neural networks, performing multiclass classification, and gaining a deep understanding of convolutional neural networks (CNN). Explore image processing, convolution intuition, and classifying photos of dogs and cats using TensorFlow. Understand the layers of deep learning neural networks and harness the power of transfer learning for advanced concepts. Engage in real-world projects like Face Mask Detection and Linear Model Implementation. Elevate your skills to master TensorFlow, enabling you to build and deploy powerful deep learning models.This masterclass is designed for individuals passionate about deep learning, whether beginners or experienced practitioners. Uncover the secrets of TensorFlow and take your understanding of deep learning to new heights!Section 1: Machine Learning ZERO to HERO - Hands-on with TensorFlowThis foundational section serves as a comprehensive introduction to machine learning using TensorFlow. It begins with essential concepts, including understanding the fundamentals of machine learning and how machines learn. The section then progresses to practical aspects, guiding learners through setting up their workstations, exploring different programming languages, and understanding the functions of Jupyter notebooks. The focus expands to include third-party libraries, with an emphasis on NumPy and Pandas for efficient data manipulation and analysis. The section concludes by introducing data visualization using Matplotlib and Seaborn, providing a solid groundwork for the subsequent sections.Section 2: Project On TensorFlow - Face Mask Detection ApplicationIn this hands-on project section, learners apply their knowledge to a real-world application by building a Face Mask Detection application using TensorFlo
Data is at the heart of our digital economy and data science has been ranked as the hottest profession of the 21st century. Whether you are new to the job market or already in the workforce and looking to upskill yourself, this five course Data Science with Python Professional Certificate program is aimed at preparing you for a career in data science and machine learning. No prior computer programming experience required!You will start by learning Python, the most popular language for data science. You will then develop skills for data analysis and data visualization and also get a practical introduction in machine learning. Finally, you will apply and demonstrate your knowledge of data science and machine learning with a capstone project involving a real life business problem.This program is taught by experts and focused on hands-on learning and job readiness. As such you will work with real datasets and will be given no-charge access to tools like Jupyter notebooks in the IBM Cloud. You will utilize popular Python toolkits and libraries such as pandas, numpy, matplotlib, seaborn, folium, scipy, scikitlearn, and more.Start developing data and analytical skills today and launch your career in data science!This course is highly practical but it won't neglect the theory. we'll start with python basics, and then understand the complete concept of environment , variables , loops , conditions and more advance concept of python programming and machine learning and we install the needed software (on Windows, Linux and Mac OS X), then we'll dive and start python programming straight away. From here onward you'll learn everything by example, by analyzing and practicing different concepts such as operator, operand, conditional statements, looping ,data management .etc, so we'll never have any boring dry theoretical lectures.The course is divided into a number of sections, each section covers a complete python programming field and complete machine lear
Learn Data Science and Machine Learning with R
This course focuses on cleaning, normalizing, and creating features to improve the performance of machine learning models.
Welcome to the Data Science Projects - Data Analysis & Machine Learning course. Data science projects course series is made from the projects that i built for my website and courses. This is not a beginner level course. This course is built for the students who learned python for data science and wants to apply what they learned but don't know where to start or for the ones who wants to practice and test their knowledge. In this course we will be building 4 data science projects which are going to be Regression, Classification, Time-Series and NLP projects. We will be covering Linear Regression, Logistic Regression, K Nearest Neighbors, Support Vector Machines, Decision Tree, Random Forests, ARIMA, Text Classification and Sentiment Analysis as machine learning algorithms in our course. All projects are going to be end to end so it will be easy to follow what we are doing step by step and I will be giving short explanations for the codes that i write. Main motivation of this course is teaching students how to do projects by theirselves. By taking this course you will be experienced in data science projects and you can apply the codes by yourself in order to build yor own project. Building projects is one of the most important ways to get into and learn Data Science. Thanks for reading, if you are interested in Data Science lets meet in the first lesson.
Embark on a Journey into the World of Data Science and Machine Learning!Welcome to the Mastering Data Science & Machine Learning Fundamentals for Beginners course, a comprehensive and illuminating exploration of the captivating realms of Data Science and Machine Learning!In today's rapidly evolving landscape, Data Science and Machine Learning are not mere buzzwords; they are the driving forces behind innovation in diverse domains, including IT, security, marketing, automation, and healthcare. These technologies underpin the very foundations of modern conveniences, from email spam filters and efficient Google searches to personalized advertisements, precise weather forecasts, and uncanny sports predictions. This course is your gateway to understanding the magic behind these advancements. Designed with students and learners in mind, this course aims to demystify complex machine learning algorithms, statistics, and mathematics. It caters to those curious minds eager to solve real-world problems using the power of machine learning. Starting with the fundamentals, the course progressively deepens your understanding of a vast array of machine learning and data science concepts. No prior knowledge or experience is required to embark on this enriching learning journey. This course not only simplifies intricate machine learning concepts but also provides hands-on guidance on implementing them successfully. Our esteemed instructors, experts in data science and AI, are your trusted guides throughout this course. They are committed to making each concept crystal clear, steering away from confusing mathematical notations and jargon, and ensuring that everything is explained in plain English. Here's a glimpse of what you'll delve into:Mastering Machine Learning FundamentalsDistinguishing between Supervised and Unsupervised L
Unlock the Power of AI: From Beginner to Advanced Machine Learning & Deep Learning ProjectsAre you ready to dive into the world of Artificial Intelligence and master Machine Learning and Deep Learning? Whether you're just starting or want to expand your AI skills, this comprehensive course is designed to guide you through hands-on projects that you can use to showcase your abilities in the real world.Key Highlights of the Course:Hands-On, Project-Based Learning: This is not just a theory-heavy course. You’ll be actively building and deploying AI models that solve real-world problems. Each module introduces a new project, ensuring you gain practical experience while learning.Perfect for Beginners to Experts: Start with the basics and move towards advanced concepts at your own pace. Whether you're new to AI or looking to deepen your knowledge, this course will meet you where you are and help you grow.Practical AI Applications: Learn to apply AI in fields like image classification, natural language processing (NLP), recommendation systems, and more, giving you a diverse skillset that can be applied to various industries.Master Deep Learning: Learn cutting-edge techniques like neural networks, CNNs (Convolutional Neural Networks), and RNNs (Recurrent Neural Networks) to handle complex tasks, opening up exciting opportunities in AI development.Deployment & Scalability: Learn to take your models from development to deployment. Understand how to use cloud platforms and scaling strategies to make your AI solutions accessible and efficient.Collaborative Learning: Engage with fellow learners, share your progress, and collaborate on projects, creating a supportive and dynamic learning environment.Expert Mentorship:<
You’ve just stumbled upon the most complete, in-depth Neural Networks for Regression course online.Whether you want to:- build the skills you need to get your first data science job- move to a more senior software developer position- become a computer scientist mastering in data science- or just learn Neural Networks to be able to create your own projects quickly....this complete Neural Networks for Regression Masterclass is the course you need to do all of this, and more.This course is designed to give you the Neural Network skills you need to become a data science expert. By the end of the course, you will understand the Multilayer Perceptron Neural Networks for Regression method extremely well and be able to apply them in your own data science projects and be productive as a computer scientist and developer.What makes this course a bestseller?Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Neural Networks for Regression course. It’s designed with simplicity and seamless progression in mind through its content.This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the Multilayer Perceptron (MLP) technique. It's a one-stop shop to learn Multilayer Networks. If you want to go beyond the core content you can do so at any time.What if I have questions?As if this course wasn’t complete enough, I
Are you preparing for a career in Data Science or Machine Learning? Mastering the technical skills is crucial, but excelling in interviews requires more than just technical knowledge. Our course, "Data Science and Machine Learning: Top Interview Questions," equips you with the essential insights and strategies to ace your interviews with confidence.In this comprehensive course, we delve into the core concepts and practical techniques that are frequently tested in interviews for data science and machine learning roles. From feature engineering and model evaluation to unsupervised learning and ensemble methods, we cover a wide range of topics essential for success in interviews.Through a series of curated hands-on exercises, you will gain proficiency in:Crafting effective feature engineering and selection strategies to optimize model performance.Understanding various performance metrics and validation techniques to assess model accuracy and generalization.Exploring unsupervised learning algorithms and ensemble methods for tackling complex data problems.Leveraging cross-validation strategies to ensure robustness and reliability of your machine learning models.Moreover, our course goes beyond technical skills to offer invaluable interview insights, tips, and best practices. You'll learn how to articulate your thought process, communicate your solutions effectively, and tackle interview questions with clarity and confidence.Whether you're a seasoned professional or a beginner in the field, "Data Science and Machine Learning: Top Interview Questions" provides you with the knowledge and skills needed to excel in your next interview and kickstart your career in data science and machine learning. Enroll now and take the next step towards your dream job!
Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.This course takes a step by step approach to teach you how to use JavaScript library, TensorFlow.js for performing machine learning and deep learning on a day-to-day basis. Beginning with an introduction to machine learning, you will learn how to create machine learning models, neural networks, and deep learning models with practical projects. You will then learn how to include a pre-trained model into your own web application to detect human emotions based on pictures and voices. You will also learn how to modify a pre-trained model to train the emotional detector from scratch using your own data.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Arish Ali started his machine learning journey 5 years ago by winning an all-India machine learning competition conducted by IISC and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has worked on some cutting-edge problems involved in multi-touch attribution modeling, market mix modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at the Bridge School of Management, which along with Northwestern University (SPS) offers a course in
Fast-paced introduction to machine learning using TensorFlow. Covers essential ML concepts with hands-on exercises and real-world examples.
This course focuses on supervised learning specifically with neural networks, covering deep neural networks, convolutional networks, and sequence classifiers.
This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLPThis is best course for any one who wants to start career in data science. with machine Learning.Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies
85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). So naturally, 85% of the interview questions come from these topics as well.This concise course, created by UNP, focuses on what matter most. This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will go a long way, understand any method easily, and create your own predictive analytics models.At the end of this course, you will be able to:independently build machine learning and predictive analytics modelsconfidently appear for exploratory data analysis, foundational data science, python interviews demonstrate mastery in exploratory data science and pythondemonstrate mastery in logistic and linear regression, the workhorses of data scienceThis course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications. Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method. This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on
A beginner-level course that introduces unsupervised learning, clustering, and Principal Component Analysis (PCA). It covers the fundamentals of clustering to group data points and find patterns, and how PCA aids in dimensionality reduction.
This course is the best for mastering the Data Science and Machine Learning from basics. If you are new to Data Science and Machine Learning, This course will help you to learn everything from Basics. This course is designed as a comprehensive and accessible introduction to two of the most transformative fields in the modern digital era. Tailored specifically for those with little to no prior experience, this course aims to demystify the core concepts of data science and machine learning while building a strong foundation for future exploration. Whether you're a student, professional, or enthusiast looking to transition into the tech industry, this course provides the essential knowledge and practical skills to get started.The course begins with a clear overview of what data science is, covering the data lifecycle—from collection and cleaning to analysis and visualization. You are introduced to key tools used in the industry, including Python programming, Jupyter notebooks, and essential libraries like Pandas, NumPy, and Matplotlib. With a hands-on approach, students engage in real-world data manipulation exercises that emphasize clarity and intuition over complexity.By the end of the course, You will have a solid understanding of how data science and machine learning work together to extract insights and drive innovation. They will be equipped with the confidence and skills to explore more advanced topics or pursue further studies in data analysis, machine learning, or artificial intelligence. This beginner-friendly course lays the groundwork for a successful journey into the exciting world of data science, empowering learners to unlock the value hidden in data and make informed, intelligent decisions.
Ready to master machine learning in Python and launch your career in data science? This hands-on, comprehensive course is the definitive guide to becoming a skilled practitioner, taking you from the fundamentals of Scikit-learn to building powerful, real-world AI models.You'll gain a deep understanding of Scikit-learn, Python's most essential and widely used machine learning library. By focusing on practical application, you will not only learn the algorithms but also how to implement the full data science workflow—a critical skill for employers.Master the Complete Data Science and Machine Learning WorkflowThis masterclass will teach you to:Prepare and Preprocess complex, real-world datasets using Python (Pandas & NumPy) and the integrated tools within Scikit-learn.Build Powerful Models using core Machine Learning algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVMs).Optimize Performance with advanced techniques like Regularization, Cross-Validation, and Principal Component Analysis (PCA) for Dimensionality Reduction.Apply both Supervised and Unsupervised Learning to solve diverse business problems in data science.Understand the AI Landscape by covering the basics of Neural Networks and their role in Deep Learning.Work through short coding exercises and large, project-style assignments, mirroring the daily work of a professional data scientist.Why Learn Machine Learning with Us?We're
This course covers various optimization techniques and their applications in machine learning.
This course teaches you how to use R to apply clustering, dimensionality reduction, and anomaly detection techniques to explore and analyze unlabeled datasets, including algorithms like k-means, hierarchical clustering, and DBSCAN.
DescriptionTake the next step in your cloud-powered AI and machine learning journey! Whether you're an aspiring data scientist, ML engineer, developer, or business leader, this course will equip you with the skills to harness AWS for scalable, real-world data science and machine learning solutions. Learn how services like SageMaker, Glue, Redshift, and QuickSight are transforming industries through data-driven intelligence, automation, and predictive analytics.Guided by hands-on projects and real-world use cases, you will:• Master foundational data science workflows and machine learning principles using AWS cloud services.• Gain hands-on experience managing data with S3, Redshift, Glue, and building models with AWS SageMaker.• Learn to train, optimize, and deploy ML models at scale using advanced tools like AutoML, hyperparameter tuning, and deep learning frameworks.• Explore industry applications in e-commerce, finance, healthcare, and manufacturing using AWS AI/ML solutions.• Understand best practices for cost management, security, and automation in cloud-based data science projects.• Position yourself for a competitive advantage by building in-demand skills at the intersection of cloud computing, AI, and machine learning.The Frameworks of the Course· Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises— designed to help you deeply understand how to leverage AWS for data science and machine learning applications.· The course includes industry-specific case studies, cloud-native tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to build, manage, and deploy ML models using AWS services.· In the first part of the course, you’ll learn the basics of data science, machine learning, and how AWS enables scalable cloud-based solutions.· In
This course covers various feature engineering techniques to get the best results from a machine learning model, including feature selection (filter, wrapper, and embedded methods) and feature extraction from image and text data.
Welcome to the exciting world of Matrix Calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will dive into the powerful mathematics that underpin many of the algorithms and techniques used in these fields. By the end of this course, you'll have the knowledge and skills to navigate the complex landscape of derivatives, gradients, and optimizations involving matrices.Course Objectives:Understand the basics of matrix calculus, linear and quadratic forms, and their derivatives.Learn how to utilize the famous Matrix Cookbook for a wide range of matrix calculus operations.Gain proficiency in optimization techniques like gradient descent and Newton's method in one and multiple dimensions.Apply the concepts learned to real-world problems in machine learning and data science, with hands-on exercises and Python code examples.Why Matrix Calculus? Matrix calculus is the language of machine learning and data science. In these fields, we often work with high-dimensional data, making matrices and their derivatives a natural representation for our problems. Understanding matrix calculus is crucial for developing and analyzing algorithms, building predictive models, and making sense of the vast amounts of data at our disposal.Section 1: Linear and Quadratic Forms In the first part of the course, we'll explore the basics of linear and quadratic forms, and their derivatives. The linear form appears in all of the most fundamental and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We will also dive into quadratic forms, which are fundamental to understanding optimization problems, which appear in regression, portfolio optimization in finance, signal processing, and control theory.The Mat
In today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python . Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. Through interactive exercises and projects, you'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your ability to work efficiently and mai
Unlock the Power of Data Science SkillsIn today's data-driven world, the ability to harness and interpret data is not just a valuable skill but a crucial advantage. Whether you're an aspiring data scientist, a seasoned professional looking to expand your skill set, or an entrepreneur aiming to leverage data for strategic decisions, our comprehensive course on data science offers a transformative learning experience.Course OverviewOur course begins with a foundational exploration of data science, introducing you to its principles and importance in various industries. You'll delve into the distinctions between data science, data engineering, and data analysis, gaining a clear understanding of their respective roles and applications. Through real-world case studies and examples, you'll discover how data science drives innovation and impacts decision-making processes across different sectors.Essential Tools and TechnologiesTo equip you with the tools needed for effective data analysis, the course covers essential programming languages such as Python and R. Whether you're manipulating data with Pandas, performing numerical operations with NumPy, or creating insightful visualizations with Matplotlib and Seaborn, you'll develop a versatile skill set that forms the backbone of data science projects.Practical Skills DevelopmentA significant focus of the course is hands-on learning. You'll gain practical experience in gathering, cleaning, and analyzing data from diverse sources. You'll hone your ability to transform raw data into actionable insights that drive business decisions.Environment Setup and Best PracticesNavigating the data science environment can be daunting, especially for beginners. That's why we guide you through the setup of Python and Jupyter Notebook on both Windows and macOS, ensuring you're equipped with the right tools from the start. You'll learn to create and manage virtual environments, enhancing your abili
Welcome to Supply Chain Analysis with Machine Learning & Neural Network course. This is a comprehensive project based course where you will learn step by step on how to perform complex analysis and visualization on supply chain dataset. This course will be mainly focusing on performing cost optimization, demand forecasting, lead time efficiency, risk management, and order quantity optimization. We will be utilizing two different models, those are LightGBM which is a machine learning model and RNN which stands for Recurrent Neural Networks. Regarding programming language, we are going to use Python alongside several libraries like Pandas for performing data modelling, Numpy for performing complex calculations, Matplotlib for visualizing the data, and Scikit-learn for implementing the machine learning models.Meanwhile, for the data, we are going to download the supply chain dataset from Kaggle. In the introduction session, you will learn basic fundamentals of supply chain analytics, such as getting to know its key objectives, getting to know models that will be used, and challenges that we commonly faced when it comes to analyzing supply chain data for example demand volatility and data integration. Then, you will continue by learning the basic mathematics and logics behind price and order quantity optimization where you will be guided step by step on how to solve a basic case study using economic order quantity equation. This session was designed to prepare your knowledge and understanding about order quantity optimization before implementing this concept to your code in the project. Afterward, you will learn about several different factors that can potentially cause supply chain disruption, such as natural disaster, economic volatility, and supplier issues. Once you’ve learnt all necessary knowledge about supply chain analytics, we will start the project. Firstly, you will be guided step by step on how to set up Google Colab IDE, then, you will also learn how to
This free course covers the fundamentals of supervised learning, including regression, classification, and clustering.
Welcome to Building Machine Learning & NLP Models for Cyber Security course. This is a comprehensive project based course where you will learn how to build intrusion detection system, predict vulnerability score, and classify cyber threat using machine learning models like Random Forest Classifier, Logistic Regression, MLP Regressor, Decision Tree Regressor, KNN, XGBoost, Naive Bayes, and K Means Clustering. This course is a perfect combination between machine learning and cyber security, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in system security. In the introduction session, you will learn about machine learning and natural language processing applications in cyber security, specifically how it can help to enhance risk management and strengthen overall security. Then, in the next section, we will learn how intrusion detection models work. This section will cover data collections, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, detecting intrusion, model evaluation, deployment, and monitoring. Afterward, we will download cyber security datasets from Kaggle, it is a platform that offers many high quality datasets from various sectors. Once everything is all set, then, we will start the project, firstly, we will clean the dataset by removing all missing values and duplicates, after we make sure the data is clean and ready to use, we will start exploratory data analysis, firstly we are going to analyze the relationship between protocol type and intrusion, which will enable us to understand how different communication protocols contribute to intrusion risk, following that, we are also going to analyze intrusion rate for each browser type, which will allow us to uncover potential vulnerabilities associated with specific browsers, then, we are going to calculate the average login attempts and failed logins for both normal and intru
Welcome to our Machine Learning Projects course! This course is designed for individuals who want to gain hands-on experience in developing and implementing machine learning models. Throughout the course, you will learn the concepts and techniques necessary to build and evaluate machine-learning models using real-world datasets.We cover basics of machine learning, including supervised and unsupervised learning, and the types of problems that can be solved using these techniques. You will also learn about common machine learning algorithms, such as linear regression, k-nearest neighbors, and decision trees.ML Prerequisites LecturesPython Crash Course: It is an introductory level course that is designed to help learners quickly learn the basics of Python programming language.Numpy: It is a library in Python that provides support for large multi-dimensional arrays of homogeneous data types, and a large collection of high-level mathematical functions to operate on these arrays.Pandas: It is a library in Python that provides easy-to-use data structures and data analysis tools. It is built on top of Numpy and is widely used for data cleaning, transformation, and manipulation.Matplotlib: It is a plotting library in Python that provides a wide range of visualization tools and support for different types of plots. It is widely used for data exploration and visualization.Seaborn: It is a library built on top of Matplotlib that provides higher-level APIs for easier and more attractive plotting. It is widely used for statistical data visualization.Plotly: It is an open-source library in Python that provides interactive and web-based visualizations. It supports a wide range of plots and is widely used for creating interactive dashboards and data visualization for the web.
Master Machine Learning & AI Engineering — From Data Analytics to Agentic AI SolutionsLaunch your career in AI with a comprehensive, hands-on course that takes you from beginner to advanced. Learn Python, data science, classical machine learning, and the latest in AI engineering—including generative AI, transformers, and LLM agents / agentic AI.Why This Course?Learn by DoingWith over 145 lectures and 21+ hours of video content, this course is built around practical Python projects and real-world use cases—not just theory.Built for the Real WorldLearn how companies like Google, Amazon, and OpenAI use AI to drive innovation. Our curriculum is based on skills in demand from leading tech employers.No Experience? No ProblemStart from scratch with beginner-friendly lessons in Python and statistics. By the end, you’ll be building intelligent systems with cutting-edge AI tools.A Structured Path from Beginner to AI Engineer1. Programming FoundationsStart with a crash course in Python, designed for beginners. You’ll learn the language fundamentals needed for data science and AI work.2. Data Science and StatisticsBuild a solid foundation in data analysis, visualization, descriptive and inferential statistics, and feature engineering—essential skills for working with real-world datasets.3. Classical Machine LearningExplore supervised and unsupervised learning, including linear regression, decision trees, SVMs, clustering, ensemble models, and reinforcement learning.4. Deep Learning with TensorFlow and KerasUnderstand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), using real code examples and exercises.5. Advanced AI Engineering and Generative AIGo beyond traditional ML to learn the latest AI tools and techniques:Transform
If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging. In this course, you will: Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language RecognitionDevelop an intuitive sense for using Machine Learning in your iOS appsCreate 7 projects from scratch in practical code-along tutorialsFind pre-trained ML models and make them ready to use in your iOS appsCreate your own custom models Add Image Recognition capability to your apps Integrate Live Video Camera Stream Object Recognition to your apps Add Siri Voice speaking feature to your apps Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit. Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experienceGet FREE unlimited hosting for one yearAnd more! This course is also full of practical use cases
Du möchtest Machine Learning verstehen und dich zum Data Scientist ausbilden lassen? Dann ist dieser Kurs genau das Richtige für Dich!Komplettpaket Machine Learning: Alle Grundlagen in Python und Machine Learning Algorithmen mitsamt Evaluation und Feature Engineering. Dabei werden Modelle aus dem Supervised Learning und Clustering betrachtet, sowie das Deep Learning und der KI. Der Fokus liegt auf den aktuellen Themen Reinforcement Learning und Natural Language Processing.Hast du dich schonmal gefragt wie es wäre den aktuell relevantesten Skill zu lernen und...von KI Trends zu profitieren?Möglichkeit auf richtig gut bezahlte Jobs zu haben?mit Python komplexe Probleme spielerisch zu lösen?in der Welt der Künstlichen Intelligenz und Deep Learning mitzuwirken?All das ist möglich im Leben eines Data Scientist. Und mit diesem Kurs bekommst du die vollständige Ausbildung dazu.Abschnitt 1: IntroductionIm ersten Abschnitt des Kurses "Machine Learning Campus: Data Science mit Python" erhältst du eine Einführung in den Kurs. Die erste Lektion bietet einen Überblick über den gesamten Kurs, damit du die Struktur und die wichtigsten Themenbereiche kennenlernen kannst. In der zweiten Lektion stellt sich der Dozent vor und teilt seine Motivation sowie seine Ziele für den Kurs mit, um dir einen persönlichen Einblick zu geben.Abschnitt 2: VorarbeitIn diesem Abschnitt legst du das Fundament für die Arbeit mit Python und den notwendigen Tools. Zunächst lernst du, wie du Python und PyCharm einrichtest. Die darauf folgenden Lektionen vertiefen deine grundlegenden Kenntnisse in Python und führen dich schrittweise in die Welt der Datenwissenschaft ein. Der Abschnitt schließt mit der Einführung in wichtige Bibliotheken wie Numpy, Pandas, Mat
Are you looking for a Machine Learning and Deep Learning course explained in Tamil?This course is designed for Tamil-speaking learners who want to master AI, ML, and DL concepts from the basics to advanced with clear explanations and practical examples.Machine Learning and Deep Learning are at the core of Artificial Intelligence (AI) and are widely used in real-world applications such as speech recognition, computer vision, chatbots, healthcare, recommendation systems, and much more.In this A to Z Tamil course, we’ll cover everything step by step in simple Tamil explanations so that even beginners can understand complex concepts easily.What You’ll Learn in This CourseIntroduction to Machine Learning (ML) & Artificial Intelligence (AI)Types of Machine Learning:Supervised LearningUnsupervised LearningReinforcement LearningML Algorithms explained in Tamil:Linear & Logistic RegressionDecision Trees & Random ForestsKNN & Naive BayesClustering (K-Means, Hierarchical)Deep Learning ConceptsArtificial Neural Networks (ANN)Convolutional Neural Networks (CNN)Recurrent Neural Networks (RNN, LSTM, GRU)Transfer Learning & Pretrained ModelsWhy Take This Course?Explained 100% in Tamil – No confusion, easy to followCovers both theory and practical insightsA to Z coverage of Machine Learning and Deep LearningBeginner-friendly with real-world exampl
HERE IS WHY YOU SHOULD TAKE THIS COURSEThis course is complete guide to both Supervised and Unsupervised learning using Python.This means,this course covers all the main aspects of practical Data Science and if you take this course you can do away withtaking other course or buying books on python based Data science .In this age of Big data companies across the globe use python to sift through the Avalache of information at their disposal..By becoming proficient in unsupervised and supervised learning in python,you can give your company a competitive edge and boost your careeer to the next level.LEARN FROM AN EXPERT DATA SCIENCE WITH 3+ YEARS OF EXPERIENCE:My Name is Aakash Singh and I had also recently published my Research Paper in INTERNATIONAL JOURNAL IJSR on Machine Learning Dataset.This course will give you robust grounding in the main aspects of Machine Learning-Clustering and Classification.NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:you will start by absorbing the most valuable python Data science basics and techniques.I use easy to understand hands on methods to simplify and address even the most difficult conceptsin python.My course will help you to implement the methods using real data obtained from different sources.After using this course you will easily use package like numpy,pandas,and mathplotlib to work with real data in python..We will go through lab section on jupyter notebook terminal .we will go through lots of real life examples for icreasing practical side knowledge of the programming and we should not neglect theory section als,which is essential for this course,by the end of this course you will be able to code in python language and feel confident with machine learning and you will also be able to create your own program amd implement were you want.Most
Embark on a transformative journey into the world of Data Analytics, Data Science, and Machine Learning, where you’ll learn the essential skills, tools, and mindsets to become a successful data professional. This comprehensive program is designed to take you from beginner to advanced, equipping you with the knowledge and practical experience needed to excel in the field.Whether you’re looking to kickstart a career in data analytics or enhance your existing skills, this course will empower you to succeed in the dynamic world of data. Join us on this exciting path and unlock your potential in just 60–100 days of disciplined learning.Why This Course MattersMost learners struggle with fragmented resources, inconsistent guidance, or theory-heavy content that doesn’t build real competence. This course solves that problem. It’s structured to provide step-by-step, cumulative, and daily progress — helping you turn knowledge into capability, and capability into career readiness.We are in the AI revolution, and every industry is transforming with tools like ChatGPT, Stable Diffusion, and AI copilots for writing, coding, design, analytics, and more. This course ensures you don’t just learn theory — you’ll build real-world solutions that make you job-ready.1. Foundations of Data Analytics, Data Science & PythonLearn how to think like a data scientist, not just how to write code.Python fundamentals: variables, loops, conditionals, functions, data structures.Clean, modular, reusable coding practices for data workflows.Importing and handling real-world datasets with Pandas and NumPy.Data types, memory optimization, and performance tuning.A-Z data cleaning and manipulation techniques: sorting, filtering, pivot tables, and charts.2. Excel, SQL, Python & Power BI Profi
This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don't understand machine learning and Artificial Neural Network from the ground up.In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered.MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN. At the end of this course, you'll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.
This course is designed to cover maximum concepts of machine learning a-z. Anyone can opt for this course. No prior understanding of machine learning is required.Bonus introductions include Natural Language Processing and Deep Learning.Below Topics are covered Chapter - Introduction to Machine Learning- Machine Learning?- Types of Machine LearningChapter - Setup Environment - Installing Anaconda, how to use Spyder and Jupiter Notebook- Installing LibrariesChapter - Creating Environment on cloud (AWS)- Creating EC2, connecting to EC2- Installing libraries, transferring files to EC2 instance, executing python scriptsChapter - Data Preprocessing- Null Values- Correlated Feature check- Data Molding- Imputing- Scaling- Label Encoder- On-Hot EncoderChapter - Supervised Learning: Regression- Simple Linear Regression- Minimizing Cost Function - Ordinary Least Square(OLS), Gradient Descent- Assumptions of Linear Regression, Dummy Variable- Multiple Linear Regression- Regression Model Performance - R-Square- Polynomial Linear RegressionChapter - Supervised Learning: Classification- Logistic Regression- K-Nearest Neighbours- Naive Bayes- Saving and Loading ML Models- Classification Model Performance - Confusion MatrixChapter: UnSupervised Learning: Clustering- Partitionaing Algorithm: K-Means Algorithm, Random Initialization Trap, Elbow Method- Hierarchical Clustering: Agglomerative, Dendogram- Density Based Clustering: DBSCAN- Measuring UnSupervised Clusters Performace - Silhouette IndexChapter: UnSupervised Learning: Association R
In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error. Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years. Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics. It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good. Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world? This course will go from basics to advance. Step by step approach will make its easy to understand Machine Learning. TIPS (for getting through the course): Watch it at 2x.Take handwritten notes. This will drastically increase your ability to retain the information.Write down the equations. If you don't, I guarantee it will just look like gibberish.Ask lots of questions on the discussion board. The more the better!Realize that most exercises will take you days or weeks to complete.Write code yourself, don't jus
This course provides a thorough introduction to the intersection of data science and machine learning, balancing theory, numerical methods (coding), and real-world applications. It is designed for students and beginners who want to build a strong foundation in the concepts, statistics, and mathematics that support modern data science and machine learning algorithms.No prior experience is required; this course starts with the fundamentals, making it an excellent choice for beginners ready to embark on their learning journey.The course covers essential topics, including:- The basics of data science- Data visualisation and storytelling- Linear and non-linear regression methods- Explore the world of classification techniques with powerful tools like decision trees, random forests, and neural networks to unlock insights from your data. - Dive into unsupervised learning, where you can discover hidden patterns and groupings in your data using innovative clustering methods like spectral clustering. By the end of this course, students will be able to:- Apply quantitative modelling and data analysis techniques to solve real-world problems.- Effectively communicate findings through data visualisation.- Demonstrate proficiency in statistical data analysis techniques used in applied engineering.- Utilise data science principles to tackle engineering challenges.- Employ modern programming languages and computational tools to analyse big data.- Understand key concepts and gain in-depth knowledge of classical machine learning algorithms.- Implement classic machine learning algorithms to create intelligent systems.
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information . Data is a fundamental part of our everyday work, whether it be in the form of valuable insights about our customers, or information to guide product,policy or systems development. Big business, social media, finance and the public sector all rely on data scientists to analyse their data and draw out business-boosting insights.Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer.It is also known as a general purpose programming language due to it's flexibility. Python is used a lot in data science. Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions.We will also be using SQL to interact with data inside a PostgreSQL Database.What you'll learnUnderstand Data Science Life CycleUse Kaggle Data SetsPerform Probability SamplingExplore and use Tabular DataExplore Pandas DataFrameManipulate Pandas DataFramePerform Data CleaningPerform Data VisualizationVisualize Qualitative DataExplore Machine Learning FrameworksUnderstand Supervised Machine LearningUse machine learning to predict value of a houseUse Scikit-LearnLoad datasets</p
A warm welcome to the Data Science, Artificial Intelligence, and Machine Learning with Python course by Uplatz.Python is a high-level, interpreted programming language that is widely used for various applications, ranging from web development to data analysis, artificial intelligence, automation, and more. It was created by Guido van Rossum and first released in 1991. Python emphasizes readability and simplicity, making it an excellent choice for both beginners and experienced developers.Data ScienceData Science is an interdisciplinary field focused on extracting knowledge and insights from structured and unstructured data. It involves various techniques from statistics, computer science, and information theory to analyze and interpret complex data.Key Components:Data Collection: Gathering data from various sources.Data Cleaning: Preparing data for analysis by handling missing values, outliers, etc.Data Exploration: Analyzing data to understand its structure and characteristics.Data Analysis: Applying statistical and machine learning techniques to extract insights.Data Visualization: Presenting data in a visual context to make the analysis results understandable.Python in Data SciencePython is widely used in Data Science because of its simplicity and the availability of powerful libraries:Pandas: For data manipulation and analysis.NumPy: For numerical computations.Matplotlib and Seaborn: For data visualization.SciPy: For advanced statistical operations.Jupyter Notebooks: For interactive data analysis and sharing code and re
TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming. What's covered: Deep learning basics: What a neuron is; how neural networks connect neurons to 'learn' complex functions; how TF makes it easy to build neural network modelsUsing Deep Learning for the famous ML problems: regression, classification, clustering and autoencodingCNNs - Convolutional Neural Networks: Kernel functions, feature maps, CNNs v DNNs RNNs - Recurrent Neural Networks: LSTMs, Back-propagation through time and dealing with vanishing/exploding gradientsUnsupervised learning techniques - Autoencoding, K-means clustering, PCA as autoencoding Working with imagesWorking with documents and word embeddingsGoogle Cloud ML Engine: Distributed training and prediction of TF models on the cloudWorking with TensorFlow estimators
Data Scientist is amongst the trendiest jobs, Glassdoor ranked it as the #1 Best Job in America in 2018 for the third year in a row, and it still holds its #1 Best Job position. Python is now the top programming language used in Data Science, with Python and R at 2nd place. Data Science is a field where data is analyzed with an aim to generate meaningful information. Today, successful data professionals understand that they require much-advanced skills for analyzing large amounts of data. Rather than relying on traditional techniques for data analysis, data mining and programming skills, as well as various tools and algorithms, are used. While there are many languages that can perform this job, Python has become the most preferred among Data Scientists.Today, the popularity of Python for Data Science is at its peak. Researchers and developers are using it for all sorts of functionality, from cleaning data and Training models to developing advanced AI and Machine Learning software. As per Statista, Python is LinkedIn's most wanted Data Science skill in the United States.Data Science with R, Python and Spark Training lets you gain expertise in Machine Learning Algorithms like K-MeansClustering, Decision Trees, Random Forest, and Naive Bayes using R, Python and Spark. Data Science Trainingencompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introductionto Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases onMedia, Healthcare, Social Media, Aviation and HR.CurriculumIntroduction to Data ScienceLearning Objectives - Get an introduction to Data Science in this module and see how Data Sciencehelps to analyze large and unstructured data with different tools.Topics:What is Data Science? What does Data Science involve?Era of Data Science Business Intelligence vs Data ScienceLife cycle
Interested in using Machine Learning in JavaScript applications and websites? Then this course is for you!This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2024. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps.Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components.This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics:Part 1 - Introduction to TensorFlow.jsPart 2 - Installing and running TensorFlow.jsPart 3 - TensorFlow.js Core ConceptsPart 4 - Data Preparation with TensorFlow.jsPart 5 - Defining a modelPart 6 - Training and Testing in TensorFlow.jsPart 7 - TensorFlow.js PredictionPart 8 - Binary ClassificationPart 9 - Multi-class ClassificationPart 10 - Conclusion & Next StepsAs a bonus, for every student, we provide you wit
In this course, you will get to understand the foundational concepts that underlie the supervised machine-learning process. You will get to understand complex topics such as:Exploratory Data Analysis, Data Transformation and Feature Scaling, Evaluation Metrics, Algorithms, trainers, and models,Underfitting and Overfitting, Cross-validation, Regularization, and much moreYou will see these concepts come alive by doing a practical machine-learning exercise, rather than by looking at presentations. We will be using a non-cloud-based machine-learning tool called Model Builder, inside of Visual Studio. There will be zero coding involved (except for the very last lesson). But even though there is little coding involved, you will still get a very detailed understanding of complex machine-learning concepts.This course requires you to have at least some theoretical exposure to the concepts of supervised and unsupervised machine learning. This course is designed to build on a basic, theoretical understanding of machine learning by doing a practical machine-learning exercise. The concepts taught in this course are foundational and will be relevant in the future, regardless of what machine learning platform or programming language you use. In the process, you will also get some exposure to Visual Studio, code projects, solutions, and the Microsoft Machine Learning ecosystem. But that is just a side benefit. This course focuses on machine learning itself, not the tools that are used.If you've already done any kind of machine learning or trained a model, this course might be too basic for you. This course may contain foundational knowledge that you may not have been taught before, but please be aware that this course is geared toward beginner and intermediate-level AI enthusiasts.
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning. we will be covering the following topics in a well crafted way: Tensors and TensorFlow on the Cloud - what neural networks, Machine learning and deep learning really are, how neurons work and how neural networks are trained. - Datalab, Linear regressions, placeholders, variables, image processing, MNIST, K- Nearest Neighbors, gradient descent, softmax and more Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Course Overview Module 1- Introduction Gcloud Introduction Labs Module 2 - Hands on GCP Labs Module 2-Datalab Module 3-Machine Learning & Tensorflow Introduction to Machine Learning, Typical usage of Mechine Learning, Types, The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables, Image processing in Tensor Flow, Image as tensors, M-NIST – Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks i
This course features 600+ Real and Most Asked Interview Questions for Machine Learning and Data Science that leading tech companies have asked. Are you ready to master machine learning and data science? This comprehensive course, Master Machine Learning and Data Science: 600+ Real Interview Questions is designed to equip you with the knowledge and confidence needed to excel in your data science career. With over 600 real interview questions and detailed explanations, you'll gain a deep understanding of core concepts, practical skills, and advanced techniques.What You’ll Learn:The essential maths behind machine learning, including algebra, calculus, statistics, and probability.Data collection, wrangling, and preprocessing techniques using powerful tools like Pandas and NumPy.Key machine learning algorithms such as regression, classification, decision trees, and model evaluation.Deep learning fundamentals, including neural networks, computer vision, and natural language processing.Whether you’re a beginner or a professional looking to sharpen your skills, this course offers practical knowledge, real-world examples, and interview preparation strategies to help you stand out in the competitive field of data science. Join us and take the next step toward mastering machine learning and data science!Sample Questions:Question 1:You are building a predictive model for customer churn using a dataset that is highly imbalanced, with a much larger number of non-churning customers than churning ones. What technique would you apply to improve model evaluation and ensure that the model is not biased by the imbalanced classes?A) Use k-fold cross-validation to assess model performance across all data splits. B) Use stratified sampling in your cross-validation to maintain the class distribution in each fold.
Do you want to become an AWS Machine Learning Engineer Using SageMaker in 30 days?Do you want to build super-powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start?Are you an absolute beginner and want to break into AI, ML, and Cloud Computing and looking for a course that includes everything you need?Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently?Do you want to leverage ChatGPT as a programmer to automate your coding tasks?If the answer is yes to any of these questions, then this course is for you!Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospectsAWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS SageMaker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows:Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) an
Hello there,Welcome to the " Machine Learning & Data Science with Python, Kaggle & Pandas " CourseMachine Learning A-Z course from zero with Python, Kaggle, Pandas and Numpy for data analysis with hands-on examplesMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning helps you stay ahead of new trends, technologies, and applications in this field.Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python, machine learning, django, python programming, machine learning python, python for beginners, data science. Kaggle, statistics, r, python data science, deep learning, python programming, django, machine learning a-z, data scientist, python for data sciencePandas is an open source Python package that is most widely used for d
Welcome to "Machine Learning and Data Science with LangChain and LLMs"! This comprehensive course is designed to equip you with the skills and knowledge needed to harness the power of LangChain and Large Language Models (LLMs) for advanced data science and machine learning tasks.In today’s data-driven world, the ability to process, analyze, and extract insights from large volumes of data is crucial. Language models like GPT have transformed how we interact with and utilize data, allowing for more sophisticated natural language processing (NLP) and machine learning applications. LangChain is an innovative framework that enables you to build applications around these powerful LLMs. This course dives deep into the integration of LLMs within the data science workflow, offering hands-on experience with real-world projects.What You Will Learn?Throughout this course, you will gain a thorough understanding of how LangChain can be utilized in various data science applications, along with the practical knowledge of how to apply LLMs in different scenarios. Starting with the basics of machine learning and data science, we gradually explore the core concepts of LLMs and how LangChain can enhance data-driven solutions.Key Learning Areas:1. Introduction to Machine Learning and Data Science: Begin your journey by understanding the core principles of machine learning and data science, including the types of data, preprocessing techniques, and model-building strategies.2. Exploring Large Language Models (LLMs): Learn what LLMs are, how they function, and their applications in various domains. This section covers the latest advancements in language models, including their architecture and capabilities in text generation, classification, and more.3. LangChain Fundamentals: Discover the potential of LangChain as a tool for
Are you looking to master Data Science,Machine Learning (ML), Deep Learning(DL) and Natural Language Processing (NLP) from the ground up? This comprehensive course is designed to take you on a journey from understanding the basics to mastering advanced concepts, all while providing practical insights and hands-on experience.What You'll Learn:Foundational Concepts: Start with the basics of ML and NLP, including algorithms, models, and techniques used in these fields. Understand the core principles that drive machine learning and natural language processing.Advanced Topics: Dive deeper into advanced topics such as deep learning, reinforcement learning, and transformer models. Learn how to apply these concepts to build more complex and powerful models.Practical Applications: Gain practical experience by working on real-world projects and case studies. Apply your knowledge to solve problems in various domains, including healthcare, finance, and e-commerce.Mathematical Foundations: Develop a strong mathematical foundation by learning the math behind ML and NLP algorithms. Understand concepts such as linear algebra, calculus, and probability theory.Industry-standard Tools: Familiarize yourself with industry-standard tools and libraries used in ML and NLP, including TensorFlow, PyTorch, and scikit-learn. Learn how to use these tools to build and deploy models.Optimization Techniques: Learn how to optimize ML and NLP models for better performance and efficiency. Understand techniques such as hyperparameter tuning, model selection, and model evaluation.Who Is This Course For:This course is suitable for anyone interested in learning machine learning and natural language processing, from beginners to advanced learners. Whether you're a student, a professional look
Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why:The course is taught by the lead instructor at the App Brewery, London's leading in-person programming bootcamp.In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build.The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems.In the curriculum, we cover a large number of important data science and machine learning topics, such as:Data
Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies. This course is an adaptation of the same course by Jose Marcial Portilla and is specially produced in collaboration with Jose for Singaporean learners. If you are a Singaporean, you are eligible for the CITREP+ funding scheme, terms and conditions apply. --------------- Note from Jose .... Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:Programming with PythonNumPy with PythonUsing pandas Data Frames to solve complex tasksUse pandas to handle Excel FilesWeb scraping with pythonConnect Python to SQLUse matplotlib and seaborn for data visual
Master the End-to-End Machine Learning Process with Python, Mathematics, and Projects — No Prior Experience NeededThis course is not just another introductory tutorial. It is a complete and intensive roadmap, carefully crafted for beginners who want to become confident and capable Machine Learning practitioners. Whether you're a student, a job-seeker, or a working professional looking to transition into AI/ML, this course equips you with the core skills, hands-on experience, and deep understanding needed to thrive in today’s data-driven world.Why This Course Is DifferentThis masterclass solves both problems by following a clear, layered, and project-oriented curriculum that blends coding, theory, and practical intuition — so you not only know what to do, but why you're doing it.You’ll go step-by-step from foundational Python to building real ML models and deploying them in real-world workflows — even touching advanced topics like ensemble models, hyperparameter tuning, regularization, and generative AI.What You’ll Learn — Inside the Masterclass#______Foundations of Machine Learning and Artificial IntelligenceWhat is ML, how it differs from AI and Deep Learning.Key ML model types: Regression, Classification, Clustering.Understanding AI applications, Gen AI, and the future of intelligent systems.Knowledge checks to reinforce conceptual understanding.#______Python Programming from Scratch – for Absolute BeginnersStarting with variables, data types, conditionals, loops, and functions.Data structures: Lists, Sets, Tuples, Dictionaries with hands-on labs.Object-oriented programming, API requests, and web scraping with BeautifulSoup.Reading and writing real-world datasets using pandas.<
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics. Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions, and finding patterns that can be modeled and can be used for predictions to see what things may look like in the future.Data Science is an amalgamation of Statistics, Tools, and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make the most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as the trending job of the 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.In This Course, We Are Going To Work On 50 Real World Projects Listed Below:Project-1: Pan Card Tempering Detector App -Deploy On Heroku
Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques?If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data.The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).About the AuthorSamuel Holt has several years' experience implementing, creating, and putting into production machine learning models for large blue-chip companies and small startups (as well as within his own companies) as a machine learning consultant.He has machine learning lab experience and holds an MEng in Machine Learning and Software Engineering from Oxford University, where he won four awards for academic excellence.Specifically, he has built systems that run in production using a combination of scikit-learn and TensorFlow involving automated customer support, implementing document OCR, detecting vehicles in the case of s
An IBM-led course that covers a variety of machine learning algorithms, including a section on decision trees and ensemble methods with hands-on labs.
Questo corso sul Data Science con Python nasce per essere un percorso completo su come si è evoluta l'analisi dati negli ultimi anni a partire dall'algebra e dalla statistica classiche. L'obiettivo è accompagnare uno studente che ha qualche base di Python in un percorso attraverso le varie anime del Data Science. Cominceremo con un ripasso delle basi di Python, a partire dallo scaricamento e installazione, all'impostazione dell'ambiente di lavoro, passando per le strutture, la creazione di funzioni, l'uso degli operatori e di alcune funzioni importanti. Passeremo poi a vedere come manipolare e gestire un dataset, estrarne dei casi oppure delle variabili, generare dei dataset casuali, calcolare delle misure statistiche di base, creare grafici con i pacchetti Matplotlib e Seaborn.Nelle sezioni successive cominciamo a entrare nel cuore del Data Science con Python, a cominciare dal preprocessing: vediamo infatti come ripulire e normalizzare un dataset, e come gestire i dati mancanti. La sezione successiva ci permette di cominciare a impostare dei modelli di machine learning con Python: vedremo tutti gli algoritmi più comuni, sia supervisionati che non supervisionati, come la regressione, semplice, multipla e logistica, il k-nearest neighbors, il Support Vector Machines, il Naive Bayes, gli alberi di decisione e il clustering. Passeremo poi ai più comuni metodi ensemble, come il Random Forest, il Bagging e il Boosting, e all'analisi del linguaggio naturale e al suo utilizzo nel machine learning per la catalogazione dei testi.
Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Linear Algebra is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science.The "data" in data science is represented using matrices and vectors, which are the central objects of study in this course.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra.In a normal STEM college program, linear algebra is split into multiple semester-long courses.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters.This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn't normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI art) and LLMs (Large Language Models) using LoRA. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can s
Hello!Welcome, and thanks for choosing How to Start & Grow Your Career in Machine Learning/Data Science!With companies in almost every industry finding ways to adopt machine learning, the demand for machine learning engineers and developers is higher than ever. Now is the best time to start considering a career in machine learning, and this course is here to guide you.This course is designed to provide you with resources and tips for getting that job and growing the career you desire.We provide tips from personal interview experiences and advice on how to pass different types of interviews with some of the hottest tech companies, such as Google, Qualcomm, Facebook, Etsy, Tesla, Apple, Samsung, Intel, and more.We hope you will come away from this course with the knowledge and confidence to navigate the job hunt, interviews, and industry jobs.***NOTE This course reflects the instructor's personal experiences with US-based companies. However, she has also worked overseas, and if there is a high interest in international opportunities, we will consider adding additional FREE updates to this course about international experiences.We will cover the following topics:Examples of Machine Learning positionsRelevant skills to have and courses to takeHow to gain the experience you needHow to apply for jobsHow to navigate the interview processHow to approach internships and full-time positionsHelpful resourcesPersonal adviceWhy Learn From Class Creatives?Janice Pan is a full-time Senior Engineer in Artificial Intelligence at Shield AI. She has published papers in the fields of computer vision and video processing and has interned at some
Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.Most of the problems nowadays as I have made a machine-learning model but what next.How it is available to the end-user, the answer is through API, but how it works?How you can understand where the Docker stands and how to monitor the build we created.This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.This course has been designed into Following sections:1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.2) Building our NLP Machine Learning model and tune the hyperparameters.3) Creating flask API and running the WebAPI in our Browser.4) Creating the Docker file, build our image and running our ML Model in Docker container.5) Configure GitLab and push your code in GitLab.6) Configure Jenkins and write Jenkins's file and run end-to-end Integration.This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.
Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser-based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.This course is project-based so you will not be learning a bunch of useless coding practices. At the end of this course, you will have real-world apps to use in your portfolio. We feel that project-based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.You can use the projects you build in this course to add to your LinkedIn profile. Give your portfolio fuel to take your career to the next level.Learning how to code is a great way to jump into a new career or enhance your current career. Coding is the new math and learning how to code will propel you forward in any situation. Learn it today and get a head start for tomorrow. People who can master technology will rule the future.
This course contains over 200 lessons, quizzes, practical examples, ... - the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R.Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course:Estimate the value of used carsWrite a spam filterDiagnose breast cancerAll code examples are shown in both programming languages - so you can choose whether you want to see the course in Python, R, or in both languages!After the course you can apply Machine Learning to your own data and make informed decisions:You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance. This course covers the important topics:RegressionClassificationOn all these topics you will learn about different algorithms. The ideas behind them are simply explained - not dry mathematical formulas, but vivid graphical explanations.We use common tools (Sklearn, NLTK, caret, data.table, ...), which are also used for real machine learning projects. What do you learn?Regression:Linear RegressionPolynomial RegressionClassification:Logistic RegressionNaive BayesDecision treesRandom ForestYou will also learn how to use Machine Lear
DescriptionTake the next step in your career! Whether you’re an up-and-coming professional, an experienced executive, Data Scientist Professional. This course is an opportunity to sharpen your Python and ML DL capabilities, increase your efficiency for professional growth and make a positive and lasting impact in the Data Related work.With this course as your guide, you learn how to:All the basic functions and skills required Python Machine LearningTransform DATA related work Make better Statistical Analysis and better Predictive Model on unseen Data.Get access to recommended templates and formats for the detail’s information related to Machine Learning And Deep Learning.Learn useful case studies, understanding the Project for a given period of time. Supervised Learning, Unsupervised Learning , ANN,CNN,RNN with useful forms and frameworksInvest in yourself today and reap the benefits for years to comeThe Frameworks of the CourseEngaging video lectures, case studies, assessment, downloadable resources and interactive exercises. This course is created to Learn about Machine Learning and Deep Learning, its importance through various chapters/units. How to maintain the proper regulatory structures and understand the different types of Regression and Classification Task. Also to learn about the Deep Learning Techniques and the Pre Trained Model.Data Preprocessing will help you to understand data insights and clean data in an organized manner, including responsibilities related to Feature Engineering and Encoding Techniques. Managing model performance and optimization will help you understand how these aspects should be maintained and managed according to the determinants and impacts of algorithm performance. This approach will also help you understand the details related to model evaluation, hyperparameter tuning, c
Welcome to this course on Machine Learning and Data Science with AWS. Amazon Web services or AWS is one of the biggest cloud computing platform where everything gets deployed to scale and action. Understanding the concepts and methods are vital, but being able to develop and deploy those concepts in forms of real life applications is something that is most weighted by the industry. Thus, here in this course, we are focused on ways you can use various cloud services on AWS to actually build and deploy you ideas into actions on multiple domains on Machine Learning and Data Science. You could be an IT professional looking for job change or upgrading your skillset or you could be a passionate learner or cloud certification aspirant, this course is for wider audience that if formed by the people who would like to learn any of these or a combination of these things-Create and Analyze dataset to find insights and spot outliers or trendsBuild Data visualization reports and dashboards by combining various visualization charts to represent data insightsDevelop machine learning models for Natural Language Processing for various applications on AWSAnd much more.Course StructureThis course consists of multiple topics that are arranged in multiple sections. In the first few sections you would learn cloud services related to Data Science and Analysis on AWS with hands on practical examples. There you would be learning about creating a crawler in Glue, Analyzing dataset using SQL in Amazon Athena. After that you would learn to prepare a dataset for creating Data Visualization charts and reports that can be used for finding critical insights from the dataset that can be used in decision making process. You will learn to create calculated fields, excluded lists and filters on AWS Quicksight, followed by some advanced charts such as Word cloud and Funnel chart.After that in Machine Learning section, you will learn
This course is designed to get an in-depth knowledge of Statistics and Probability for Data Science and Machine Learning point of view. Here we are talking about each and every concept of Descriptive and Inferential statistics and Probability. We are covering the following topics in detail with many examples so that the concepts will be crystal clear and you can apply them in the day to day work. Extensive coverage of statistics in detail: The measure of Central Tendency (Mean Median and Mode) The Measure of Spread (Range, IQR, Variance, Standard Deviation and Mean Absolute deviation) Regression and Advanced regression in details with Hypothesis understanding (P-value) Covariance Matrix, Karl Pearson Correlation Coefficient, and Spearman Rank Correlation Coefficient with examplesDetailed understanding of Normal Distribution and its propertiesSymmetric Distribution, Skewness, Kurtosis, and KDE. Probability and its in-depth knowledge Permutations and Combinations Combinatorics and Probability Understanding of Random Variables Various distributions like Binomial, Bernoulli, Geometric, and Poisson Sampling distributions and Central Limit Theorem Confidence IntervalMargin of ErrorT-statistic and F-statisticSignificance tests in detail with various examples Type 1 and Type 2 ErrorsChi-Square Test ANOVA and F-statisticBy completing this course we are sure you will be very much proficient in Statistics and able to talk to anyone about stats with confidence apply the knowledge in your day to day work.
This course covers the fundamentals of tree-based models, including decision trees, random forests, and gradient boosting. You will learn how to build, tune, and evaluate these models using Python's scikit-learn library.
This is the first course in the Machine Learning Specialization. It provides a broad introduction to modern machine learning, including supervised learning (linear regression, logistic regression, neural networks, and decision trees). You will build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
A lecture focusing on the role of optimization in machine learning, covering various algorithms and their properties.
This course teaches you how to build end-to-end machine learning applications. It covers topics such as feature engineering, intermediate machine learning models, and recommender systems.
This course provides a comprehensive exploration of AI-powered data engineering, equipping participants with the skills to design, orchestrate, and deploy intelligent data pipelines tailored for ML and DL applications. The training also covers advanced tools and platforms used in building AI-driven pipelines, including TensorFlow Extended (TFX), MLflow, Apache Airflow, and Kubeflow.
This course includes a module on Ensemble Learning, covering decision trees and random forests.
This course introduces the importance of quality data in machine learning. It covers techniques to retrieve, clean, and apply feature engineering to data, preparing it for preliminary analysis and hypothesis testing.
This course provides a comprehensive overview of how machine learning functions in embedded systems. It teaches students how to train neural networks and deploy them to microcontrollers, a field also known as TinyML. The course is designed for beginners with no prior machine learning experience, but some familiarity with Arduino and microcontrollers is recommended.
This course provides a deep dive into gradient boosting and the popular XGBoost library. You'll learn how to build and tune high-performance machine learning models.
This course teaches how to use the Intel® VTune™ Profiler to analyze and optimize the performance of AI and machine learning workloads. It covers identifying performance bottlenecks in deep learning frameworks like TensorFlow and PyTorch.
A free online course that introduces the theory and applications of machine learning algorithms with a focus on policy applications and issues. The course includes hands-on applications using R and Python.
An advanced course focusing on the practical application of data science and machine learning to solve real-world cybersecurity problems. It includes over 30 hands-on labs.
A free intermediate-level course covering linear algebra, calculus, and probability for machine learning, with an included certificate.
This course introduces the theoretical foundations and algorithmic developments in stochastic optimization for machine learning. It covers basic convex optimization theories and focuses on stochastic approximation and its accelerations in statistical and machine learning models.
A series of video lectures from Stanford University on how machine learning can be used for causal inference, including estimating treatment effects and designing targeted interventions.
This training covers both text classification and NER, explaining entity types and the differences between rule-based and machine learning approaches. It includes hands-on labs for implementing NER with spaCy and customizing models for specific applications.
Deep dives into the latest machine learning research papers. Understand cutting-edge AI research with clear explanations.
Explore all AI and machine learning topics.
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