Beginner Machine Learning

The original Stanford ML course taught by Andrew Ng

Comprehensive ML course covering regression, classification, clustering, deep learning, NLP, reinforcement learning.

Text classification, sentiment analysis, topic modeling, text generation with spa Cy, NLTK.

Complete Machine Learning & Data Science Bootcamp 2025

Algorithmic Trading with Machine Learning

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.

Linear Algebra for Machine Learning

Machine Learning for Business Analytics

Machine Learning and AI with Python Web Apps

Google Cloud Machine Learning Complete Course

Feature Engineering for Machine Learning

AutoML: Automated Machine Learning

This course covers Support Vector Machines (SVM) from basic to advanced kernel-based models. It is designed for those who want to apply machine learning to real-world business problems and includes topics like hyperparameter tuning and model performance evaluation.

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.

Learn Support Vector Machines in R Studio, from basic SVM models to advanced kernel-based SVM models. This course is for those who want to apply machine learning to real-world business problems using the R programming language.

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.

This course covers a wide range of machine learning algorithms in R, including a dedicated section on tree-based methods.

Get instant access to a workbook on Data Science, follow along, and keep for reference Introduce yourself to our community of students in this course and tell us your goals with data science Encouragement and celebration of your progress every step of the way: 25% &gt; 50% &gt; 75% &amp; 100%30 hours of clear and concise step-by-step instructions, lessons, and engagement This data science course provides participants with the knowledge, skills, and experience associated with Data Science. Students will explore a range of data science tools, algorithms, Machine Learning, and statistical techniques, with the aim of discovering hidden insights and patterns from raw data in order to inform scientific business decision-making.What&nbsp; you will learn:Data Science and Its Types Top 10 Jobs in Data Science Tools of Data Science Variables and Data in Python Introduction to Python Probability and Statistics Functions in Python Operator in Python Data Frame with Excel Dictionaries in Python Tuples and loops Conditional Statement in Python Sequences in Python Iterations in Python Multiple Regression in Python Linear Regression Libraries in Python Numpy and SK Learn Pandas in PythonK-Means Clustering Clustering of Data Data Visualization with Matplotlib Data Preprocessing in Python Mathematics in Python Data Visualization with Plotly What is Deep Learning?Deep Learning Neural Network TensorFlow PostgreSQL Machine Learning and Data Science<

Practice questions to prepare for Generative AI LL Ms Associate (NCA-GENL)!This certification is designed to validate foundational knowledge and practical skills in working with large language models (LL Ms) and generative AI. This certification is ideal for professionals aiming to develop expertise in deploying and managing LLM-based solutions. Key focus areas include understanding transformer-based architectures, prompt engineering techniques for guiding model responses, and leveraging modern pretrained models to solve a range of natural language processing (NLP) tasks, such as text generation, token classification, and sentiment analysis. The certification covers best practices for working with human-labeled data and strategies for optimizing models for specific applications. This certification is ideal for those looking to strengthen their understanding of generative AI and advanced technologies within the rapidly evolving AI landscape.About the course Prepare yourself for success in the Generative AI LL Ms certification with this comprehensive mock exam course. This course is specifically designed to help you master the key concepts and skills needed to excel in the rapidly growing field of Generative AI, focusing on Large Language Models (LL Ms).This course features six carefully crafted mock exams that closely mirror the format, difficulty, and scope of the actual certification exam. Each mock exam contains a diverse set of questions that test your knowledge on various topics, including the fundamentals of Generative AI, architecture and deployment of LL Ms, model training and fine-tuning, ethical considerations, and specific tools and platforms for AI development.What sets this course apart is the detailed explanations provided for each question. After completing each exam, you will not only see which answers you got right or wrong but also receive in-depth explanations that clarify why certain answers are correct. This approach

Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of fields in which deep learning has the most influence today is Computer Vision.Object detection, Image Segmentation, Image Classification, Image Generation &amp; People Counting To understand why Deep Learning based Computer Vision is so popular; it suffices to take a look at the different domains where giving a computer the power to understand its surroundings via a camera has changed our lives.Some applications of Computer Vision are:Helping doctors more efficiently carry out medical diagnosticsenabling farmers to harvest their products with robots, with&nbsp; the need for very little human intervention,Enable self-driving cars Helping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brain Creation of art&nbsp; with GANs, VAEs, and Diffusion Models Data analytics in sports, where players' movements are monitored automatically using sophisticated computer vision algorithms.The demand for Computer Vision engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using TensorFlow 2 (the world's most popular library for deep learning, built by Google) and Hugging Face. We shall start by understanding how to build very simple mo

This new course is the updated version of the previous course Deep Learning for Computer Vision with Tensorflow2.X.It contains new classes explaining in detail many state of the art algorithms for image classification and object detection.The course was entirely written using Google Colaboratory(Colab) in order to help students that don't have a GPU card in your local system, however you can follow the course easily if you have one.This time the course starts explaining in detail the building blocks from ConvNets which are the base for image classification and the base for the feature extractors in the latest object detection algorithms.We're going to study in detail the following concepts and algorithms:- Image Fundamentals in Computer Vision,- Load images in Generators with TensorFlow,- Convolution Operation,- Sparsity Connections and parameter sharing,- Depthwise separable convolution,- Padding,- Conv2D layer with TensorFlow,- Pooling layer,- Fully connected layer,- Batch Normalization,- ReLU activation and other functions,- Number of training parameters calculation,- Image Augmentation, etc- Different ConvNets architectures such as:&nbsp; * Le Net5,&nbsp; * Alex Net,&nbsp; * VGG-16,&nbsp; * Res Net,&nbsp; * Inception,&nbsp; * The lastest state of art Vision Transformer (ViT)- Many practical applications using famous datasets and sources such as:&nbsp; * Covid19 on X-Ray images,&nbsp; * CIFAR10,&nbsp; * Fashion MNIST,&nbsp; * BCCD,&nbsp; * COCO dataset,&nbsp; * Open Images Dataset V6 through Voxel Fifty

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

Deep learning has become one of the most popular machine learning techniques in recent years, and PyTorch has emerged as a powerful and flexible tool for building deep learning models. In this course, you will learn the fundamentals of deep learning and how to implement neural networks using PyTorch.Through a combination of lectures, hands-on coding sessions, and projects, you will gain a deep understanding of the theory behind deep learning techniques such as deep Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs). You will also learn how to train and evaluate these models using PyTorch, and how to optimize them using techniques such as stochastic gradient descent and backpropagation. During the course, I will also show you how you can use GPU instead of CPU and increase the performance of the deep learning calculation.In this course, I will teach you everything you need to start deep learning with PyTorch such as:Num Py Crash Course Pandas Crash Course Neural Network Theory and Intuition How to Work with Torchvision datasets Convolutional Neural Network (CNNs)Long-Short Term Memory (LSTMs)and much more Since this course is designed for all levels (from beginner to advanced), we start with basic concepts and preliminary intuitions.By the end of this course, you will have a strong foundation in deep learning with PyTorch and be able to apply these techniques to various real-world problems, such as image classification, time series analysis, and even creating your own deep learning applications.

Welcome to the Full Stack Data Science &amp; Machine Learning Boot Camp 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:<

This course provides&nbsp;you to&nbsp;be able to build Deep Neural Networks models&nbsp;for different business domains&nbsp;with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras.&nbsp;We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google Face Net, Open Face and Facebook Deep Face).This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don't have to be attend any ML course before.

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.&nbsp;S, you will finally get to know why Neural Network performs better &amp;&nbsp;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 &amp;&nbsp;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 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 Mc Kinsey, 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

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 installations Chapter2: Useful Machine Learning libraries (Num Py, Pandas &amp; Matplotlib)Chapter3: Preprocessing Chapter4: Machine Learning Types Chapter5: Supervised Learning: Classification Chapter6: Supervised Learning: Regression Chapter7: Unsupervised Learning: Clustering Chapter8: Model Tuning Furthermore, 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.

Hello there,Welcome to the " Complete Data Science &amp; Machine Learning A-Z with Python " Course Machine Learning &amp; Data Science all in one course with Python Data Visualization, Data Analysis Pandas &amp; Numpy, Kaggle Machine 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 i Phone’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 science Pandas is an open source Python package that is most widely used for

Hello there,Welcome to the “Machine Learning &amp; Data Science with Python &amp; Kaggle | A-Z” course.Data Science &amp; Machine Learning A-Z &amp; Kaggle with Heart Attack Prediction projects and Machine Learning Python projects Machine 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 i Phone’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 science Python 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 use Then 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 course This 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 Num Py or Sci Py 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

Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow &amp;&nbsp;Deep&nbsp;Learning Expert!Join a live online community of over 900,000+ students and a course taught by a TensorFlow expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks.TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD. By taking this course you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow Developer!Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch!):The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer.This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter. 0 — TensorFlow Fundamentals Introduction to tensors (creating tensors)Getting information from tensors (tensor attributes)Manipulating tensors (tensor operations)Tensors and Num PyUsing @tf.function (a way to speed up your regular Python functions)Using GP Us with Tensor Flow1 — Neural Network Regression with TensorFlow Build TensorFlow sequential models with multiple layers Prepare data for use with a machine learning model

Hello there,Welcome to the “Complete Machine Learning &amp; Data Science with Python | A-Z” course Use Scikit, learn Num Py, 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 &amp; data science with python | a-z, machine learning a-z, Complete machine learning &amp; 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 science It’s hard to imagine our lives without machine learning Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the i Phone’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 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

Do you want to master Num Py and unlock your potential in data science? This course is your comprehensive, hands-on introduction to the foundational library of modern Python computing!Num Py 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 Num Py for scientific computing. We start with the basics and rapidly move into advanced techniques crucial for complex data science tasks.Foundation: Introduction to Num Py 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, django Welcome 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 &amp; 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 Future Data 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 demand Udemy 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

Welcome to this comprehensive hands-on course on YOL Ov10 for real-time object detection! YOL Ov10 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 YOL Ov10 for various object detection tasks.Throughout the course, you will learn how to set up and use YOL Ov10, label and create datasets, and train the model with custom data. The course is divided into three main parts:Part 1: Learning to Use YOL Ov10 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 YOL Ov10 models to detect objects in images. We will cover the following:Setting up the environment and installing necessary packages.Downloading pre-trained YOL Ov10 models.Performing object detection on sample images.Visualizing and interpreting detection results.Part 2: Labeling and Making a Dataset with Robo Flow In the second part, we will focus on creating and managing custom datasets using Robo Flow. This section will teach you how to:Create a project workspace on the Robo Flow 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 YOL Ov10.Part 3: Training with Custom Datasets The final section of the course is dedicated to training YOL Ov10 with your custom datasets. You will learn how to:Configure the training process, including setting parameters such as epochs and batch size.Train the YOL Ov10 model using your labeled dataset from Robo Flow.Monitor training progress and evaluate the trained model.

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 tool In this video, we are going to install r programming with rstudio in Windows Platform.Lab 01 R Installation and Concepts In 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 Concepts In this video, we are going to learn the necessary concepts of RProgramming.Video 3_R Progrming Computations In this tutorial, we will be learning about several mathematical algorithms and computations.Lab 02 R P

Hello there,Welcome to the “Python Programming: Machine Learning, Deep Learning | Python” course Python, machine learning, python programming, django, ethical hacking, data analysis, python for beginners, machine learning python, python bootcamp Python Machine Learning and Python Deep Learning with Data Analysis, Artificial Intelligence, OOP, and Python Projects Complete 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 i Phone’s Siri, are all technologies that function based on machine learning algorithms and mathe

Python, Java, Py Charm,&nbsp;Android Studio and MNIST.&nbsp;Learn to code and build apps! Use machine learning models in hands-on projects. &nbsp;A wildly successful Kickstarter funded this course Explore machine learning concepts.&nbsp;Learn how&nbsp;to use TensorFlow 1.4.1 to build, train, and test&nbsp;machine learning models.&nbsp;We explore Python 3.6.2&nbsp;and Java 8&nbsp;languages, and how to use Py Charm&nbsp;2017.2.3 and Android Studio 3 to build apps.&nbsp;A machine learning framework for everyone If 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 first There are next to&nbsp;no&nbsp;courses on big platforms&nbsp;that focus on&nbsp;mobile&nbsp;machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.We&nbsp;provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and&nbsp;expand&nbsp;upon what I teach. Other&nbsp;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.&nbsp;No prior knowledge is required We 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&nbsp;demand&nbsp;than supply Machine 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

Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep 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. Other courses and tutorials have tended to stay away from pure TensorFlow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network Basics TensorFlow Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks Auto Encoders Reinforcement Learning OpenAI Gymand much more! 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 graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CP Us or GP Us in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system

Learn Python for Data Science &amp; 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 Num Py, Pandas, and Matplotlib +Num Py —&nbsp; 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.Num Py 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

Interested in the field of Machine Learning? Then this course is for you!Designed &amp; Crafted by AI&nbsp;Solution&nbsp;Expert with 15 +&nbsp;years of relevant and hands on experience into Training ,&nbsp;Coaching and Development.Complete Hands-on&nbsp;AI Model Development with Python.&nbsp; Course Contents are:Understand Machine Learning in depth and in simple process. Fundamentals of Machine Learning Understand the Deep Learning Neural Nets with Practical Examples.Understand Image Recognition and Auto Encoders.Machine learning project Life Cycle Supervised &amp; Unsupervised Learning Data Pre-Processing Algorithm Selection Data Sampling and Cross Validation Feature Engineering Model Training and ValidationK -Nearest Neighbor AlgorithmK- Means Algorithm Accuracy Determination Visualization using Seaborn You will be trained to develop various algorithms for supervised &amp; unsupervised methods such as&nbsp; KNN , K-Means , Random&nbsp;Forest, XG Boost 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 analysis Building and deploying Machine Learning models from scratch Exploring Data Science techniques, including data cleaning, visualization, and analysis Working with Big Data Analytics tools to handle massive datasets Implementing AI solutions for real-world projects and business applications Understanding key concepts in Deep Learning, Neural Networks, and Predictive Analytics Who This Course is For:Anyone passionate about leveraging AI and Big Data to make smarter decisions Why Choose This Course:Hands-on projects and real-world examples Learn from beginner-friendly to advanced concepts in a structured way Focused on practical applications that can boost your career or business Certificate after course complete By 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!

This Data Science Course is a comprehensive program designed to provide learners with the essential skills and knowledge needed to understand, analyze, and apply data-driven solutions in the modern world. This course is carefully structured to take you from the basics of data handling to advanced concepts in data analysis, visualization, and predictive modeling.Beginning with fundamental programming skills in Python and R, the course introduces you to core topics such as data cleaning, exploratory data analysis, and statistical methods. From there, you’ll gain hands-on experience with popular tools and libraries, including Pandas, Num Py, Matplotlib, and Scikit-Learn, to manipulate and visualize data effectively. Machine learning concepts are introduced gradually, ensuring a strong foundation in supervised and unsupervised learning, model building, and evaluation techniques.Beyond technical skills, this course emphasizes practical, real-world applications of data science across industries such as business, healthcare, finance, and technology. Through projects and case studies, you will develop the ability to extract actionable insights, solve complex problems, and communicate findings clearly to both technical and non-technical audiences.By the end of the program, you will not only understand the theory but also be confident in applying data science methods to real datasets. Whether you are a student, professional, or aspiring data scientist, the Data Science Course by Shimwa Bonheur equips you with the tools and confidence to thrive in today’s data-driven world.

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

Growing Importance of Deep Learning Deep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more. Made for Anyone Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning. Code As You Learn This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along. I highly recommend you to type every line of code when you are listening to the videos as this will help a lot in getting used to the syntax. Gradual Learning Style The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start. Diagram-Driven Code This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefu

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

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

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 Algebra Then 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 &amp; Operations on Vectors Matrices &amp; Operations on Matrices Determinant and Inverse Solving Systems of Linear Equations Norms &amp; Basis Vectors Linear Independence Matrix Factorization Orthogonality Eigenvalues and Eigenvectors Singular 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….

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 Overview Our 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 Technologies To 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 Num Py, 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.&nbsp; &nbsp;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 Practices Navigating 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

Welcome to the Python for Data Science Bootcamp: From Zero to Hero. In this course, we're going to learn how to use Python for Data Science. In this practical course, we'll learn how to collect data, clean data, make visualizations and build a machine learning model using Python.The main goal of this course is to take your programming and analytical skills to the next level to build your career in Data Science. To achieve this goal, we're going to solve hundreds of exercises and many cool projects that will help you put into practice all the programming concepts used in Data Science.We'll learn the top Python Libraries used in Data Science such as Pandas, Numpy and Scikit-Learn and we will use them to learn to solve tasks data scientists&nbsp; deal with on a daily basis (Data Cleaning, Data Visualization, Data Collection and Model Building)This course covers 4 main sections.1. Python for Data Science Crash Course: In the first section, we'll learn all the Python core concepts you need to know for Data Science. We'll learn how to use variables, lists, dictionaries and more.2. Python for Data Analysis: We'll learn Python libraries used for data analysis such as Pandas and Numpy. Both are great tools for exploring and working with data. We'll use Pandas and Numpy to deal with data science tasks such as cleaning and preparing data.3. Python for Data Visualization: In the third section, we'll learn how to make static and interactive visualizations with Pandas. Also, I'll show you some techniques to properly make data visualization.4. Machine Learning with Python: In the fourth section, we'll learn Scikit-Learn by solving a text classification problem in Python. This is the most popular machine learning library in Python and we'll not only learn how to implement machine learning algorithms in Python but also we'll learn the core concepts behind the most common algorithms using practical examples.Bonus (Basic Web Scraping with Python): Remember that at the end o

Welcome to the gateway to your journey into Python for Machine Learning &amp; 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 &amp; Deep Learning? Enroll now and take the first step towards becoming a proficient AI practitioner!

This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTMs), Gated Recurrent Units(GRUs), etc. TensorFlow, Keras, Google Colab, Real World Projects and Case Studies on topics like Regression and Classification have been described in great detail. Advanced Case studies like Self Driving Cars will be discussed in great detail. Currently the course has few case studies.The objective is to include at least 20 real world projects soon.&nbsp; Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail.&nbsp; The ultimate goal of this course is to make the learner able to solve real world problems using deep learning. After completion of this course the Learner shall also be able to pass the Google TensorFlow Certification Examination which is one of the prestigious Certification. Learner will also get the certificate of completion from Udemy after completing the Course. After taking this course the learner will be expert in following topics.&nbsp;a) Theoretical Deep Learning Concepts.b) Convolutional Neural Networksc) Long-short term memoryd) Generative Adversarial Networkse) Encoder- Decoder Modelsf) Attention Modelsg) Object detectionh) Image Segmentationi) Transfer Learningj) OpenCV using Pythonk) Building and deploying Deep Neural Networks&nbsp;l) Professional Google TensorFlow developer&nbsp;m) Using Google Colab for writing Deep Learning coden) Python programming for Deep Neural Networks The Learners are advised to practice the TensorFlow code as they watch the videos on Programming from this course.&nbsp;First Few sections have been uploaded, The course is in updation phase and the remaining sections will be added soon.

Welcome to our Python &amp; 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!

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-Packages How to work with R-data types What is R Data Frame, Matrices, Vectors, etc?How to work with Data Frames How to perform join and merge operations on Data Frames How 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.

Master Deep Learning and Computer Vision: From Foundations to Cutting-Edge Techniques Elevate your career with a comprehensive deep dive into the world of machine learning, with a focus on object detection, image classification, and object tracking.This course is designed to equip you with the practical skills and theoretical knowledge needed to excel in the field of computer vision and deep learning. You'll learn to leverage state-of-the-art techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and advanced object detection models like YOL Ov8.Key Learning Outcomes:Fundamental Concepts:Grasp the core concepts of machine learning and deep learning, including supervised and unsupervised learning.Understand the mathematical foundations of neural networks, such as linear algebra, calculus, and probability theory.Computer Vision Techniques:Master image processing techniques, including filtering, noise reduction, and feature extraction.Learn to implement various object detection models, such as YOL Ov8, Faster R-CNNs, and SSD.Explore image classification techniques, including CNNs architectures like Res Net, Inception, and Efficient Net.Dive into object tracking algorithms, such as SORT, DeepSORT, and Kalman filtering.Practical Projects:Build real-world applications, such as license plate recognition, traffic sign detection, and sports analytics.Gain hands-on experience with popular deep learning frameworks like TensorFlow and PyTorch.Learn to fine-tune pre-trained models and train custom models for specific tasks.Why Choose This Course?Expert Instruction: Learn from experienced ins

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 (MLPs) 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

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&nbsp;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&nbsp;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&nbsp; STATA&nbsp;to apply Machine Learning Models in Credit Risk Scenario. Because we know that many of you are already familiar with STATA&nbsp;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!

PyTorch&amp;Hugginface Deep Learning Course(Colab Hands-On) Welcome to PyTorch Deep Learning From Zero To Hero Series.If you have already mastered the basic syntax of python and don't know what to do next, this course will be a rocket booster to skyrocket your programming skill to a business applicable level.In this course, you will be able to master implementing deep neural network from the very beginning(simple perceptron) to BERT transfer learning/Google's T5 by using PyTorch and Hugging Face yourself by colab. Each Section will have one assignment for you to think and code yourself. The Agenda is below.&nbsp; &nbsp;Agenda:Introduction Google Colaboratory Neuron Perceptron Make Your Perceptron Trainable Normalize Data Activation Function Loss Function Gradient Descent Elegant PyTorch Gradient Descent Final Project Final Project Explained Multi Layer Perceptron(MLPs)One Hot Encoding Prepare data for MLPs Define MLPs Train &amp; Evaluate MLPs Final Project for MLPFCNN ExplainedFCNN LOVE Letters Classification using MLPs Final Project For FCNNCNN ExplainedCNN Prepare data(Fashion MNIST) CNNs Define Model CNNs Train&amp;Evaluate ModelCNN Inference Final Project For CNNRNN ExplainedRNN Prepare dataRNN Define ModelRNN Train ModelRNN InferenceBERT Sesame StreetBERT Prepare Data IMDBBERT Model definitionBERT Model TrainingBERT Model Evaluation<p

Machine learning is an extremely hot area in Artificial Intelligence and Data Science. There is no doubt that Neural Networks are the most well-regarded and widely used machine learning techniques. A lot of Data Scientists use Neural Networks without understanding their internal structure. However, understanding the internal structure and mechanism of such machine learning techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new Neural Networks for different projects. This course is the easiest way to understand how Neural Networks work in detail. It also puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well-paid data scientists. Why learn Neural Networks as a Data Scientist? Machine learning is getting popular in all industries every single month with the main purpose of improving revenue and decreasing costs. Neural Networks are extremely practical machine learning techniques in different projects. You can use them to automate and optimize the process of solving challenging tasks. &nbsp;What does a data scientist need to learn about Neural Networks? &nbsp;The first thing you need to learn is the mathematical models behind them. You cannot believe how easy and intuitive the mathematical models and equations are. This course starts with intuitive examples to take you through the most fundamental mathematical models of all Neural Networks. There is no equation in this course without an in-depth explanation and visual examples. If you hate math, then sit back, relax, and enjoy the videos to learn the math behind Neural Networks with minimum efforts. It is also important to know what types of problems can be solved with Neural Networks. This course shows different types of problems to solve using Neural Networks including clas

Master Real-Time Object Detection with Deep Learning Dive into the world of computer vision and learn to build intelligent video analytics systems. This comprehensive course covers everything from foundational concepts to advanced techniques, including:Video Analytics Basics: Understand the 3-step process of capturing, processing, and saving video data.Object Detection Powerhouse: Explore state-of-the-art object detection models like Haar Cascade, HOG, Faster RCNN, R-FCN, SSD, and YOLO.Real-World Applications: Implement practical projects like people footfall tracking, automatic parking management, and real-time license plate recognition.Deep Learning Mastery: Learn to train and deploy deep learning models for image classification and object detection using frameworks like TensorFlow and Keras.Hands-On Experience: Benefit from line-by-line code walkthroughs and dedicated support to ensure a smooth learning journey.Exciting News!We've just added two new, hands-on projects to help you master real-world computer vision applications:Real-Time License Plate Recognition System Using YOL Ov3: Dive deep into real-time object detection and recognition.Training a YOL Ov3 Model for Real-Time License Plate Recognition: Learn to customize and train your own YOL Ov3 model. Don't miss this opportunity to level up your skills!Why Enroll?Industry-Relevant Skills: Gain in-demand skills to advance your career in AI and machine learning.Practical Projects: Build a strong portfolio with real-world applications.Expert Guidance: Learn from experienced instructors and get personalized support.

This comprehensive course will take you on a journey from the foundational concepts of machine learning and TensorFlow to the creation of advanced, real world deep learning models. I'll start with the basics, giving you a solid understanding of how neural networks work, and progressively build up your skills to tackle complex problems in computer vision, natural language processing (NLP), and more. Through a series of hands-on labs, projects, and practical examples, you'll learn to not only build and train models but also to understand the "why" behind the code, enabling you to confidently solve new and challenging problems.This course is designed for anyone with a basic understanding of Python programming who wants to build a career in machine learning and artificial intelligence. Whether you're a student, a software developer, or a data analyst, this course will provide you with the practical skills and foundational knowledge to become a proficient TensorFlow practitioner.Why Take This Course?Artificial Intelligence is transforming industries worldwide, and deep learning lies at its core. TensorFlow, developed by Google, has become the industry standard library for building and deploying AI applications at scale. This course provides a step by step learning journey, blending theory with hands-on coding so you not only understand concepts but can also implement them in real world projects.By the end of this course, you’ll have the knowledge and confidence to:Understand the foundations of deep learning and TensorFlow.Build simple and complex neural networks from scratch.Train, evaluate, and optimize models using modern techniques.Work with Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced architectures.Deploy machine learning models in real-world scenarios.What You’ll L

Do you want to learn about Web Development and Machine learning at the same time? With this course you can do exactly that and more!This course was funded by a wildly successful Kickstarter With the Deep Learning of Angular 2 and TensorFlow, You will learn about Javascript frameworks for creating websites and create Apps driven by Machine Learning by learning TensorFlow as well as Py Charm, Python, Android Studio and more!About TensorFlow: ​​We use frameworks like TensorFlow that make it easy to build, train, test, and use machine learning models. TensorFlow makes machine learning so much more accessible to programmers everywhere You can expect a complete and comprehensive course that guides you first through the basics, then through some simple models. You will end up with a ​portfolio of apps driven by machine learning, as well as the know-how to create more and expand upon what we build together.About Angular 2: ​​Java Script is one of the fundamental languages of the web. Java Script is easy to program in but some tasks are difficult. Java Script frameworks are built to make these difficult tasks easier. In this course you will learn how to code with ​Angular.js 2,​​ ​a powerful framework that makes building web apps a breeze​​. In this course you will learn web programming fundamentals and other valuable skill boosting career knowledge.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.Also, now included in this course are bonus courses of other related topics, such as Cand Java! You get more content at a great price!En

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 Num Py. 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 projects The 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!

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:&nbsp; &nbsp; &nbsp;1. Introduction to Machine Learning&nbsp; &nbsp; &nbsp;2. Anaconda – An Overview &amp; Installation&nbsp; &nbsp; &nbsp;3. Jupyter Lab – An Overview&nbsp; &nbsp; &nbsp;4. Python – An Overview&nbsp; &nbsp; &nbsp;5. Linear Algebra – An Overview&nbsp; &nbsp; &nbsp;6. Statistics – An Overview&nbsp; &nbsp; &nbsp;7. Probability – An Overview&nbsp; &nbsp; &nbsp;8. OO Ps – An Overview&nbsp; &nbsp; &nbsp;9. Important Libraries – An Overview This 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.

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 topics Practice 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 programs Happy 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 Algebra Then 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 &amp; Operations on Vectors Matrices &amp; Operations on Matrices Determinant and Inverse Solving Systems of Linear Equations Norms &amp; Basis Vectors Linear Independence Matrix Factorization Orthogonality Eigenvalues and Eigenvectors Singular 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….

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

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&nbsp;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 AI At 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 certification Are there any pre-requisites?Students must have a basic knowledge of undergraduate level mathematics in areas like Linear Algebra, Probability, Statistics and Calculus.&nbsp; 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

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.

Short Summary about the need and importance of the Course Linear 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 below Mathematics for Machine Learning: 1. Introduction to linear Algebra Difference 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 expression Geometric 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 Equation Definition 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

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 determination Ready 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 Linked In at Adam Cole Adam Cole BSc MBCS.

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 AP Is, 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?Java Server 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.Java Server Pages are built on top of the Java Servlets API, so like Servlets, JSP also has access to all the powerful Enterprise Java AP Is, 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.Audience This 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.

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 algorithms Machine 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 i Phone’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

Are you interested in Artificial Intelligence (AI), Machine Learning and Artificial Neural Network?Are you afraid of getting started with Deep Learning because it sounds too technical?Have you been watching Deep Learning videos, but still don’t feel like you “get” it?I’ve been there myself! I don’t have an engineering background. I learned to code on my own. But AI still seemed completely out of reach.This course was built to save you many months of frustration trying to decipher Deep Learning. After taking this course, you’ll feel ready to tackle more advanced, cutting-edge topics in AI.In this course:We assume as little prior knowledge as possible. No engineering or computer science background required (except for basic Python knowledge). You don’t know all the math needed for Deep Learning? That’s OK. We'll go through them all together - step by step.We'll "reinvent" a deep neural network so you'll have an intimate knowledge of the underlying mechanics. This will make you feel more comfortable with Deep Learning and give you an intuitive feel for the subject.We'll also build a basic neural network from scratch in PyTorch and PyTorch Lightning and train an MNIST model for handwritten digit recognition.After taking this course:You’ll finally feel you have an “intuitive” understanding of Deep Learning and feel confident expanding your knowledge further.If you go back to the popular courses you had trouble understanding before (like Andrew Ng's courses or Jeremy Howards' fast.ai course), you’ll be pleasantly surprised at how much more you can understand.You'll be able to understand

Module-1​Welcome to the Pre-Program Preparatory Content Session-1:​1) Introduction​2) Preparatory Content Learning ExperienceMODULE-2​INTRODUCTION TO PYTHON Session-1:​Understanding Digital Disruption Course structure​1) Introduction​2) Understanding Primary Actions​3) Understanding es &amp; Important Pointers Session-2:​Introduction to python​1) Getting Started — Installation​2) Introduction to Jupyter Notebook​The Basics Data Structures in Python3) Lists​4) Tuples​5) Dictionaries​6) Sets Session-3:​Control Structures and Functions​1) Introduction​2) If-Elif-Else​3) Loops​4) Comprehensions​5) Functions​6) Map, Filter, and Reduce​7) Summary Session-4:​Practice Questions​1) Practice Questions I​2) Practice Questions II Module-3​Python for Data Science Session-1:​Introduction to Num Py​1) Introduction​2) Num Py Basics​3) Creating Num Py Arrays​4) Structure and Content of Arrays​5) Subset, Slice, Index and Iterate through Arrays​6) Multidimensional Arrays​7) Computation Times in Num Py and Standard Python Lists​8) Summary Session-2:​Operations on Num Py Arrays​1) Introduction​2) Basic Operations​3) Operations on Arrays​4) Basic Linear Algebra Operations​5) Summary Session-3:​Introduction to Pandas​1) Introduction​2) Pandas Basics​3) Indexing and Selecting Data​4) Merge and Append​5) Grouping and Summarizing Data frames​6) Lambda function &amp; Pivot tables​7) Summary Session-4:​Getting and Cleaning Data​1) Introduction2) Reading Delimited and Relational Databases​3) Reading Data from Websites​4) Getting Data from AP Is​5) Reading Data from PDF Files​6) Cl

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

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 Basics TensorFlow detailed,Keras,Sonnet etc Artificial Neural Networks Types of Neural network Feed forward network Radial basis network Kohonen Self organizing maps Recurrent neural Network Modular Neural networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks Machine 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

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,&nbsp; I started my career as analyst as gradually moved to data scientist hence&nbsp; I&nbsp;can understand what are programming prerequisites for data scientist. This course is created for absolute beginners of data science and machine learning.&nbsp; It covers all aspect of python languages required in data science machine learning and deep learning.

¡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

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&nbsp;Generativa y Agentes IA. ¡Únete a nosotros y desbloquea el potencial de la Inteligencia Artificial!

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.

Get instant access to a 69-page Machine Learning workbook containing all the reference material Over 9 hours of clear and concise step-by-step instructions, practical lessons, and engagement Introduce yourself to our community of students in this course and tell us your goals Encouragement &amp; celebration of your progress: 25%, 50%, 75%, and then 100% when you get your certificate What 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 Learning Supervised Machine Learning Unsupervised Machine Learning Semi-Supervised Machine Learning Types of Supervised Learning: Classification Regression Types of Unsupervised Learning: Clustering Association Data Collection Data Preparing Selection of a Model Data Training and EvaluationHPT in Machine Learning Prediction in MLDPP in ML Need of DPP Steps in DPP Python Libraries Missing, Encoding, and Splitting Data in ML Python, Java, R,and C ++How to install python and anaconda?Interface of Jupyter Notebook Mathematics in Python Euler's Number and Variables Degree into Radians and Radians into Degrees in Python Printing Functions in Python Feature Scaling for ML<p

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 competitions Check 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

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 analysishealthcaregenomics Why should you take this course?&nbsp;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 skill Comfortable with data science libraries like Numpy and Matplotlib For the advanced section, probability knowledge is requiredWHAT&nbsp;ORDER&nbsp;SHOULD&nbsp;I&nbsp;TAKE&nbsp;YOUR&nbsp;COURSES&nbsp;IN?Check out the lecture "Machine Learning and AI&nbsp;Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)UNIQUE&nbsp;FEATURES Every line of code explained in detail - email me any time if you disagree Less than 24 hour

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!

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

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 content Introduction 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

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.=&gt;&nbsp;"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 &amp; 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)Funktionen Variablen,...Data Science:Lese Daten ein Erstelle anschauliche VisualisierungenÜberzeuge deine Kollegen durch überzeugende PDF-Reports Diverse 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 Engineer You 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 Git Hub, 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!

TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. So, if you’re a Python developer who is interested in learning how to create applications and perform image processing using TensorFlow, then you should surely go for this Learning Path. Packt’s Video Learning Path is 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. The highlights of this Learning Path are: Learn how to create image processing applications using free tools and libraries Perform advanced image processing with Tensor FlowAP Is Understand and optimize various features of TensorFlow by building deep learning state-of-the-art models Let's take a quick look at your learning journey. This Learning Path starts off with an introduction to image processing. You will then walk through graph tensor which is used for image classification. Starting with the basic 2D images, you will gradually be taken through more complex images, colors, shapes, and so on. You will also learn to make use of Python API to classify and train your model to identify objects in an image. Next, you will learn about convolutional neural networks (CNNs), its architecture, and why they perform well in the image take. You will then dive into the different layers available in TensorFlow. You will also learn to construct the neural network feature extractor to embed images into a dense and rich vector space. Moving ahead, you will learn to construct efficient CNNs architectures with CNNs Squeeze layers and delayed downsampling. You will learn about residual learning with skip connections and deep residual blocks, and see how to implement a deep residual neural network for image recognition. Next, you will find out about Google’s Incep

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.&nbsp; 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.&nbsp; 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.&nbsp; 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.&nbsp; 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.&nbsp; Then, we tackle linear tr

Disclaimer:The second of this course demonstrates techniques using Jupyter Notebooks from Anaconda.&nbsp; 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!&nbsp;&nbsp;We are delighted to have over 20,000 students in over 160 different countries.&nbsp; I'm genuinely touched by&nbsp;the overwhelmingly positive and thoughtful reviews.&nbsp; It's such a privilege to share and introduce this important topic with everyday people in&nbsp;a clear and understandable way.&nbsp;I'm also excited to announce that I&nbsp;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&nbsp;students!)... I've got you covered.&nbsp;Most importantly: To make this course "real", we've expanded.&nbsp; In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections!&nbsp;&nbsp;We hope you enjoy the new content!&nbsp;&nbsp; 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.&nbsp; 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.&nbsp; 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

Computer Vision With Deep Learningرؤية الكمبيوتر باستخدام التعلم العميقDescription This is a complete course that will prepare you to work in Computer Vision Using Deep Learning. We will cover the fundamentals of Deep Learning/ computer Vision and its applications, this course is designed to reduce the time for the learner to Learn Computer Vision using Deep learning.هذه دورة كاملة ستعدك للعمل في رؤية الكمبيوتر باستخدام التعلم العميق. سنغطي أساسيات التعلم العميق/رؤية الكمبيوتر وتطبيقاتها، وقد تم تصميم هذه الدورة لتقليل الوقت الذي يستغرقه المتعلم لتعلم رؤية الكمبيوتر باستخدام التعلم العميق.What Skills will you Learn:In this course, you will learn the following skills:Understand the Math behind Deep Learning Algorithms.Understand How computer vision Algorithms works.Write and build computer vision Algorithms using Deep learning technologies.Use opensource libraries.We will cover:Fundamentals of Computer Vision.Image Preprocessing.Deep Neural Network (DNNs) - PyTorch . Convolutional Neural Network (CNNs)- TensorFlow.Semantic Segmentation.Object Detection.Instance Segmentation.Pose Estimation.Generative AI.Face Recognition.If you do not have prior experience in Machine Learning OR&nbsp;Computer vision, that's NO PROBLEM!. This course is complete and concise, covering the fundamental Theory and needed coding knowledge. Let's work together to learn Computer Vision Using Deep Learning.إذا لم تكن لديك خبرة سابقة في التعلم الآلي أو رؤية الك

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 GANs 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