Intermediate Deep Learning

Comprehensive deep learning specialization by Andrew Ng. Master neural networks, CNNs, RNNs, and modern deep learning architectures.

Master deep learning using the PyTorch framework. Build and train neural networks for computer vision and NLP applications.

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

Deep Learning A-Z: Hands-On Neural Networks

Natural Language Processing with Deep Learning

Chatbot Development with Python and Deep Learning

This course covers RNNs, LSTMs, and GRUs in TensorFlow. It includes projects on time series prediction, music generation, language translation, image captioning, spam detection, and action recognition.

Hi this is Abhilash Nelson and I am thrilled to introduce you to my new course Deep Learning and Neural Networks using Python: For Dummies The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. Ranging from google suggestions, translations, ads, movie recommendations, friend suggestions, sales and customer experience so on and so forth. There are tons of other applications too. No wonder why "Deep Learning" and "Machine Learning along with Data Science" are the most sought after talent in the technology world now a days.But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas needs to be studied prior to that. Its just like someone tries to make you believe that, you should learn the working of an Internal Combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user friendly control pedals extending from engine like clutch, brake, accelerator, steering wheel etc. And with a bit of experience, you can easily drive a car. The basic know how about the internal working of the engine is of course an added advantage while driving a car, but its not mandatory. Just like that, in our deep learning course, we have a perfect balance between learning the basic concepts along the implementation of the built in Deep Learning Classes and functions from the Keras Library using the Python Programming Language. These classes, functions and AP Is are just like the control pedals from the car engine, which we can use easily to build an efficient deep learning model.Lets now see how this course is organized and an overview about the list of topics included.We will be starting with few theory sessions in which we will see an overview about the Deep Learning an

Welcome to Complete Python Data Science, Deep Learning, R Programming course.Python Data Science A-Z, Data Science with Machine Learning A-Z, Deep Learning A-Z, Pandas, Numpy and R Statistics Data science,  python data science, r statistics, machine learning, deep learning, data visualization, Num Py, pandas, data science with r, r, complete data science, maths for data science, data science a-z Data Science A-Z, Python Data Science with Machine Learning, Deep Learning, Pandas, Numpy, Data visualization, and RReady for the Data Science career? Are you curious about Data Science and looking to start your self-learning journey into the world of data?Are you an experienced developer looking for a landing in Data Science!In both cases, you are at the right place! The two most popular programming tools for data science work are Python and R at the moment. It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and open-source.Train up with a top-rated data science course on Udemy. Gain in-demand skills and help organizations forecast product and service demands for the future. From machine learning to data mining to data analysis, we’ve got a data science course to help you progress on your career path.R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.With my full-stack Data Science course, you will be able to learn R and Python together.If you have some programming experience, Python might be the language for you. R was built as a statistical language, it suits much better to do statistical learning with R programming.But do not worry! In this course, yo

Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Linear Algebra is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science.The "data" in data science is represented using matrices and vectors, which are the central objects of study in this course.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra.In a normal STEM college program, linear algebra is split into multiple semester-long courses.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters.This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn't normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI art) and LL Ms (Large Language Models) using LoRA. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can s

This course is designed to make you proficient in training and evaluating deep learning based object detection models. Specifically, you will learn about Faster R-CNNs, SSD and YOLO models. For each of these models, you will first learn about how they function from a high level perspective. This will help you build the intuition about how they work. After this, you will learn how to leverage the power of TensorFlow 2 to train and evaluate these models on your local machine. Finally, you will learn how to leverage the power of cloud computing to improve your training process. For this last part, you will learn how to use Google Cloud AI Platform in order to train and evaluate your models on powerful GP Us offered by google. I designed this course to help you become proficient in training and evaluating object detection models. This is done by helping you in several different ways, including :Building the necessary intuition that will help you answer most questions about object detection using deep learning, which is a very common topic in interviews for positions in the fields of computer vision and deep learning.By teaching you how to create your own models using your own custom dataset. This will enable you to create some powerful AI solutions.By teaching you how to leverage the power of Google Cloud AI Platform in order to push your model's performance by having access to powerful GP Us.

A warm welcome to the Machine Learning and Data Science Interview Guide course by Cloud Excellence Academy.We provides this unique list of Data Science Interview Questions and Answers to help you prepare for the Data Scientist and Machine Learning Engineer interviews. This exhaustive list of important data science interview questions and answers might play a significant role in your interview preparation career and helping you get your next dream job. The course contains real questions with fully detailed explanations and solutions. Not only is the course designed for candidates to achieve a full understanding of possible interview questions, but also for recruiters to learn about what to look for in each question response. Why Data Science Job ?According to Glassdoor, a career as a Data Scientist is the best job in America! With an average base salary of over $120,000, not only do Data Scientists earn fantastic compensation, but they also get to work on some of the world's most interesting problems! Data Scientist positions are also rated as having some of the best work-life balances by Glassdoor. Companies are in dire need of filling out this unique role, and you can use this course to help you rock your Data Scientist Interview!Let's get started!Unlike others, We offer details explanation to each and every questions that will help you to understand the question100% money back guarantee (Unconditional, we assure that you will be satisfied with our services and be ready to face the data science interview).The Course highlights100 Questions on Machine Learning Algorithms , Use Cases ,Scenarios, Regularizations etc.75 Questions on Deep Learning ( ANNs , CNNs , RNNs , LSTMs , Transformer)100 Questions on Statistics and Probability 50 Question on Pyth

Tauche ein in die kreative Seite der Künstlichen Intelligenz – mit Generativen Neuronalen Netzwerken (GANs), Autoencodern und Adversarial Attacks. In diesem praxisorientierten Kurs lernst du, wie du mit Python, TensorFlow 2.14 und Keras eigene Deep-Learning-Modelle entwickelst, trainierst und sogar „hackst“.Nach einer kurzen Einführung in die Grundlagen von Machine Learning und Deep Learning, baust du Schritt für Schritt eigene neuronale Netze auf – von klassischen Deep Neural Networks bis hin zu verschiedenen Arten von GANs. Du verstehst nicht nur, wie diese Modelle funktionieren, sondern setzt sie auch selbst um – mit zahlreichen spannenden Coding-Sessions.Neben der Generierung realistischer Daten mit Variational Autoencodern (VAEs) und der Datenkomprimierung mit klassischen Autoencodern, wirst du auch lernen, wie neuronale Netze durch gezielte Adversarial Attacks ausgetrickst werden können – und wie man sich dagegen schützt.Dieser Kurs richtet sich an alle, die ein solides Verständnis im Deep Learning aufbauen und moderne generative Modelle praktisch umsetzen möchten. Egal ob Data Science Student, KI-Enthusiast oder Entwickler – hier wirst du gefordert und gefördert.Das wirst du lernen:Grundlagen von Machine Learning & Deep Learning Eigene Deep Neural Networks mit TensorFlow & Keras entwickeln Adversarial Generative Networks (GANs) verstehen und implementieren Adversarial Attacks: Netzwerke gezielt angreifen & absichern Daten komprimieren mit Autoencodern (AE)Realistische Daten generieren mit Variational Autoencodern (VAEs)Arbeiten in Python (über Anaconda oder andere Installationen)Werde jetzt Teil der KI-Zukunft – mit deinem eigenen generativen Netzwerk.Let’s code the future – wir sehen uns im Kurs!Hinweis:Python wird im Kurs mit Anac

In this course, we will implement a neural network from scratch, without dedicated libraries. Although we will use the python programming language, at the end of this course, you will be able to implement a neural network in any programming language. We will see how neural networks work intuitively, and then mathematically. We will also see some important tricks, which allow stabilizing the training of neural networks (log-sum-exp trick), and to prevent the memory used during training from growing exponentially (jacobian-vector product). Without these tricks, most neural networks could not be trained. We will train our neural networks on real image classification and regression problems. To do so, we will implement different cost functions, as well as several activation functions. This course is aimed at developers who would like to implement a neural network from scratch as well as those who want to understand how a neural network works from A to Z. This course is taught using the Python programming language and requires basic programming skills. If you do not have the required background, I recommend that you brush up on your programming skills by taking a crash course in programming. It is also recommended that you have some knowledge of Algebra and Analysis to get the most out of this course. Concepts covered : Neural networks Implementing neural networks from scratch Gradient descent and Jacobian matrix The creation of Modules that can be nested in order to create a complex neural architecture The log-sum-exp trick Jacobian vector product Activation functions (ReLU, Softmax, Log Softmax, ...) Cost functions (MSE Loss, NLL Loss, ...) This course will be frequently updated, with the addition of bonuses. Don't wait any longer before launching yourself i

Atenção! Nas aulas deste curso é utilizada a versão 1.x do TensorFlow, sendo possível acompanhar as aulas utilizando essa versão. Adicionalmente, disponibilizamos o código atualizado considerando a versão 2.x. Em breve pretendemos regravar todas as aulas deste cursoA área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, e Bay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o

Pada kursus ini, teman-teman akan belajar mengenai pengolahan citra dengan menggunakan Bahasa Python. Materi pada kursus ini didesain sesederhana mungkin agar teman-teman dapat lebih mudah dalam memahami materi yang disampaikan. Selain materi yang mudah dipahami dan dipelajari, materi pada kursus ini akan dikembangkan dan ditambahkan secara terus menerus seiring berkembangnya bidang computer vision atau pengolahan citra. Materi yang disajikan berawal dari materi paling sederhana yaitu pre-processing citra dan dilanjutkan dengan deep learning.Pada pre-processing citra, teman-teman akan belajar mengenai rotasi, shifting(pergeseran pixel), flipping, ruang warna dan masih banyak lagi. Pada materi ruang warna, teman-teman akan belajar juga mengenai perhitungan matematika secara manual sebelum implementasi dengan menggunakan python. Pada materi deep learning, teman-teman akan belajar mengenai Neural Network atau NN dan Convolutional Neural Network (CNNs). Materi yang akan dipelajari pada Neural Network berupa perhitungan matematika dari forward pass dan backward pass. Selain perhitungan manual, teman-teman juga akan belajar bagaimana cara mengimplementasikan Neural Network dengan menggunakan Bahasa Python dengan library PyTorch. Pada materi Convolutional Neural Network, teman-teman akan mempelajari bagaimana sebuah mesin mempelajar sebuah data dan membuat sebuah sistem Artificial Intelligence (AI) secara sederhana. Materi Convolutional Neural Network yang disajikan antara lain, bagaimana penerapan dengan menggunakan Bahasa Python dengan library PyTorch dan bagaimana contoh-contoh penggunaan Convolutional Neural Network dalam kehidupan sehari-hari.

Selamat datang di program Pelatihan Data Science dengan Deep Learning dan Python Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dengan titik fokus pemanfaatan Deep Learning untuk model machine learning dan data science.Peserta diharapkan sudah menguasai pemrograman Python dasar implementasi machine learning dan data science dengan menggunakan Python. Kami juga menyediakan konten mengenai Pelatihan Data Science dan Machine Learning Dengan Python yang ada di Udemy ini.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Konsep dan teori mengenai Deep Learning Pengenalan TensorFlow dan Keras Dasar Tensor TensorFlow Pemanfaatan GPU dan TPU pada komputasi TensorFlow dan Keras Pembuat Model dan Layer Untuk TensorFlow Training dan evaluasi Deep Learning pada TensorFlow Pengenalan dan instalasi PyTorch Pemanfaatan GPU dan TPU pada komputasi PyTorch Membangun model Deep Learning dengan PyTorch Training dan evaluasi Deep Learning pada PyTorch Penggunaan Tensor Board untuk visualisasi model pada TensorFlow dan PyTorch Penerapan Hyperparameter Tuning pada TensorFlow dan Keras Penerapan Hyperparameter Tuning pada PyTorch Penggunaan Tensor Board untuk implementasi Hyperparameter Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada we

As aplicações de Inteligência Artificial (IA) com Python têm desempenhado um papel significativo no setor financeiro, trazendo uma série de benefícios e transformando a forma como as instituições lidam com dados e tomam decisões. Aqui está um resumo da importância dessas aplicações em finanças:1. Tomada de Decisão Baseada em Dados:   - A IA com Python capacita as instituições financeiras a tomar decisões mais informadas e precisas, utilizando algoritmos avançados para analisar grandes conjuntos de dados. Isso resulta em estratégias mais eficazes de investimento, gestão de riscos aprimorada e decisões mais fundamentadas.2. Previsão de Mercado e Tendências:   - Algoritmos de machine learning e modelos de IA são utilizados para prever movimentos de mercado, identificar tendências e realizar análises preditivas. Isso auxilia investidores, traders e gestores de ativos na identificação de oportunidades e na mitigação de riscos.3. Detecção de Fraudes e Segurança:   - Sistemas de IA são empregados para detectar padrões suspeitos e atividades fraudulentas em transações financeiras. Essa capacidade de análise em tempo real contribui para a segurança das transações e a proteção contra atividades fraudulentas.4. Gestão de Portfólio Automatizada:   - Algoritmos de IA e aprendizado de máquina são usados para criar e otimizar automaticamente portfólios de investimento. Esses sistemas automatizados podem ajustar dinamicamente as alocações de ativos com base em condições de mercado em constante mudança.5. Atendimento ao Cliente e Chatbots:   - A IA é aplicada em chatbots e assistentes virtuais para melhorar o atendimento ao cliente. Essas soluções são capazes de responder a consultas, fornecer informações sobre contas e até mesmo realizar transações simples, melhorando a eficiência e a experiência do cliente.6. Análise de Sentimento e Mí

Welcome to the Complete Deep Learning Course 2021 With 7+ Real Projects 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 google colab and Jupiter 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 Deep Learning.Google Colab Anaconda Jupiter Notebook Activation Function.Keras.Pandas.Seaborn.Feature scaling.Matplotlib.Scikit-Learn Sigmoid Function.Tanh Function.ReLU Function.Leaky Relu Function.Exponential Linear Unit Function.Swish function.Corpora.NLTK.TensorFlow 2.0Tokenization.Spacy.PoS tagging.NER.Stemming and lemmatization.Semantics and topic modelling.Sentiment analysis techniques.Lexicon-based methods.Rule-based methods.Statistical methods.Machine learning methods.</

Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.We are going to execute following real-life projects,Kaggle Bike Demand Prediction from Kaggle competition Automation of the Loan Approval process The famous IRIS Classification Adult Income Predictions from US Census Dataset Bank Telemarketing Predictions Breast Cancer Predictions Predict Diabetes using Prima Indians Diabetes Dataset Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others. As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning? Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,Understanding of the overall landscape of Data Science and Machine Learning Different types of Data Analy

Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platform Lets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning Mechanism Then we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on th

Comprehensive Course Description:Electrification was undeniably one of the greatest engineering feats of the 20th century. The invention of the electric motor dates back to 1821, with mathematical analysis of electrical circuits following in 1827. However, it took several decades for the full electrification of factories, households, and railways to begin. Fast forward to today, and we are witnessing a similar trajectory with Artificial Intelligence (AI). Despite being formally founded in 1956, AI has only recently begun to revolutionize the way humanity lives and works.Similarly, Data Science is a vast and expanding field that encompasses data systems and processes aimed at organizing and deriving insights from data. One of the most important branches of AI, Machine Learning (ML), involves developing systems that can autonomously learn and improve from experience without human intervention. ML is at the forefront of AI, as it aims to endow machines with independent learning capabilities.Our "Data Science &amp; Machine Learning Full Course in 90 Hours" offers an exhaustive exploration of both data science and machine learning, providing in-depth coverage of essential concepts in these fields. In today's world, organizations generate staggering amounts of data, and the ability to store, analyze, and derive meaningful insights from this data is invaluable. Data science plays a critical role here, focusing on data modeling, warehousing, and deriving practical outcomes from raw data.For data scientists, AI and ML are indispensable, as they not only help tackle large data sets but also enhance decision-making processes. The ability to transition between roles and apply these methodologies across different stages of a data science project makes them invaluable to any organization.What Makes This Course Unique?This course is designed to provide both theoretical foundations and practical, hands-on experience. By the end of the

Jetzt neu: Zusätzlicher Bonus zum Thema Deep Learning (Neuronale Netze) mit Python, TensorFlow und Keras!Dieser Kurs enthält über 300 Lektionen, Quizze, Praxisbeispiele, ... - der einfachste Weg, wenn du Machine Learning lernen möchtest. Schritt für Schritt bringe ich dir maschinelles Lernen bei. In jedem Abschnitt lernst du ein neues Thema - zuerst die Idee / Intuition dahinter, und anschließend den Code sowohl in Python als auch in R.Machine Learning macht erst dann richtig Spaß, wenn man echte Daten auswertet. Deswegen analysierst du in diesem Kurs besonders viele Praxisbeispiele:Schätze den Wert von Gebrauchtwagen Schreibe einen Spam-Filter Diagnostiziere Brustkrebs Schreibe ein Programm, was die Bedeutung von Adjektiven lernt Lese Zahlen aus Bildern ein Alle Codebeispiele werden dir beiden Programmiersprachen gezeigt - du kannst also wählen, ob du den Kurs in Python, R, oder in beiden Sprachen sehen möchtest!Nach dem Kurs kannst du Machine Learning auch auf eigene Daten anwenden und eigenständig fundierte Entscheidungen treffen:Du weißt, wann welche Modelle in Frage kommen könnten und wie du diese vergleichst. Du kannst analysieren, welche Spalten benötigt werden, ob zusätzliche Daten benötigt werden, und weißt, die die Daten vorab aufbereitet werden müssen. Dieser Kurs behandelt alle wichtigen Themen:Regression Klassifizierung Clustering Natural Language Processing Bonus: Deep Learning (nur für Python, weil die Tools hier sehr viel ausgereifter sind)Zu allen diesen Themen lernst du verschiedene Algorithmen kennen. Die Ideen dahinter werden einfach erklärt - keine trockenen, mathematischen Formeln, sondern anschauliche, grafische Erklärungen.Wir verwenden hierbei g

Selamat datang di program pelatihan data science dan machine learning dengan Python!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan Python.Bagi rekan - rekan yang belum menguasai pemrograman Python, pelatihan juga memberikan konten pemrograman dasar untuk&nbsp; Python sehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman Python, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Pemrograman Python Python Virtual Environment Pengolahan dan Analisa Data - Numpy dan Pandas Topik Khusus - Numpy dan Pandas - Database Visualisasi Data dengan memanfaatkan library Matplotlib, Seaborn dan Bokeh Topik Khusus Visualisasi Data Time Series Dataset, Pra-Proses dan Pengurangan Dimensi Feature (Dimensionality Reduction)Permasalahan dan Penyelesaian Kasus Linear Regression Permasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Hyperparameter Tuning Untuk Model Machine Learning Ensemble Methods Reinforcement Learning Automated Machine Learning (AutoML)Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan

Data Science is an interdisciplinary field that leverages statistical analysis, data exploration, and machine learning techniques to derive knowledge and meaningful insights from data.Definition of Data Science:Data Science encompasses various processes, including data acquisition, thorough analysis, and informed decision-making.Data Science involves the identification and interpretation of data patterns to make predictive assessments.Through the application of Data Science, organizations can achieve:1. Improved decision-making processes, enabling the selection between alternatives (A or B) with greater confidence.2. Predictive analysis that anticipates future events or trends, aiding in proactive planning.3. Discovery of hidden patterns and valuable information within datasets, leading to actionable insights.Applications of Data Science:Data Science finds extensive application across diverse industries such as banking, consultancy, healthcare, and manufacturing.Examples of Data Science applications include:1. Optimizing route planning for shipping purposes.2. Anticipating potential delays in flights, ships, trains, etc., through predictive analysis.3. Crafting personalized promotional offers for customers.4. Determining the best time to deliver goods for maximum efficiency.5. Forecasting future revenue for a company.6. Analyzing the health benefits of specific training regimens.7. Predicting election outcomes.Data Science Integration in Business:Data Science can be seamlessly integrated into various facets of business operations where relevant data is available, including:1. Consumer goods industries for market analysis and consumer behavior prediction.2. Stock markets for financial analysis and forecasting.3. Industrial settings for process optimization and quality control.4. Political scenarios for opinion

Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places?Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask?If the answer to any of the above questions is "YES", then this course is for you.Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using TensorFlow and OpenCV. As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely.To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places.This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say :"Short, sweet, to the point course"The same techniques can be used in :Skin cancer detection Normal pneumonia detection Brain

Hello there,Welcome to the “Machine Learning Python with Theoretically for Data Science” course.Machine Learning with Python in detail both practically and theoretically with machine learning project for data science Machine learning courses teach you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. machine learning, python, data science, machine learning python, python data science, machine learning a-z, python for data science and machine learning bootcamp, python for data science, complete machine learning, machine learning projects,Use Scikit-Learn, Num Py, Pandas, Matpl

Machine learning and Deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains.In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects).Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students' skills in Python language will also increase and they will become more proficient in it.In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGD Classifier, ... and different models. Also, they will use artificial neural networks for modeling to do the projects.The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course.Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains.Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then

La Visión por Computador o Computer Vision (en inglés) es uno de los primeros objetivos que tuvo la programación desde sus inicios y, sobre todo, desde que se planteó la utilización del procesado automático en las cadenas de montaje. Desde discriminar la madurez de las frutas por su color, hasta reconocer patrones biométricos, pasando por los pulsómetros ópticos, o el reconocimiento de matrículas. Las utilidades de la Visión por Computador están sólo limitadas por la imaginación humana. En los últimos años, con el aumento del conocimiento en la denominada Ciencia de los Datos, se han desarrollados nuevos (y no tan nuevos) métodos de Aprendizaje para que sean las máquinas las que puedan tomar decisiones en base al procesado de la imagen que sus ojos tecnológicos les proporciona. El Machine Learning y, el siguiente paso, el Deep Learning ha supuesto una ventaja mayor si cabe en la autonomía de las máquinas. Trabajaremos con un famoso set de datos denominado MNIST, y que contiene 60.000 ejemplos de números manuscritos con su correspondiente etiqueta del número que representan. Cada número esta formado por una matriz de píxeles de 28x28 con valores entre 0 y 255 para la intensidad del trazo. En el curso vamos a analizar una buena cantidad de métodos y algoritmos de Machine Learning, como Naïve Bayes, Random Forest, Support Vector Machine, K Nearest Neighbours o Redes Neuronales y sistemas de pre-procesado de la información, como PCA, SVD o HOG. También trabajaremos algunos sistemas de Deep Learning, como H2O o TensorFlow (de Google) para el tratamiento de esta información de imágenes. Espero que os guste el curso y que disfrutéis aprendiendo los entresijos de la Visión por Computador y el Aprendizaje Profundo y Automático.

Yapay zeka alanına giriş yapmak ve "öğrenen" uygulamalar geliştirmek istiyorsanız derin öğrenme yöntemlerini öğrenmek için sizi temelden ileri seviyeye kadar teorik anlatım ve pratik uygulamaları içeren bu kapsamlı "Derin Öğrenmeye Giriş" eğitimime davet ediyorum.Eğitimi bitirdiğinizde, derin öğrenmenin temellerini, yapay sinir ağı modelleri oluşturma ve geliştirme adımlarını ve başarılı yapay öğrenme projelerini nasıl gerçekleştirebileceğinizi öğreneceksiniz. Uygulayacağımız yöntemler:Temel yapay sinir ağları, Evrişimli sinir ağları (CNNs), Özyinelemeli sinir ağları (RNNs), Uzun-kısa vadeli bellek modeli (LSTMs), Makine öğrenmesinde optimizasyon ve regülarizasyon yöntemlerini, Kapsül ağları, Pekiştirmeli öğrenme (RL), Çekişmeli üretici ağları (GANs) Tüm bu yöntemleri Python programlama dili kullanarak TensorFlow ve gerisinde çalışan Keras kütüphanelerinde uygulayacaksınız. Yapay zeka ve derin öğrenme çoklu endüstrileri geliştirmekte ve dönüştürmektedir. Bu dersi tamamladıktan sonra, bunu işinize uygulamak için yaratıcı yollar bulabilirsiniz.

Para entender cómo organizaciones como Google, Amazon e incluso Udemy utilizan el Machine Learning y la inteligencia artificial (IA) para extraer el significado y los conocimientos de enormes conjuntos de datos , este curso de Machine Learning te proporciona lo esencial. Según Glassdoor y Indeed, los científicos de datos ganaron un sueldo medio de 120.000 dólares, ¡y eso es solo la norma!Cuando se trata de ser atractivo, los científicos de datos ya lo son. En un mercado laboral altamente competitivo, es difícil retenerlos una vez contratados. Las personas con una mezcla única de formación científica, experiencia informática y capacidad de análisis son difíciles de encontrar.Al igual que los "quants" de Wall Street de los años ochenta y noventa, se espera que los científicos de datos de hoy en día tengan un conjunto de habilidades similares. Las personas con formación en física y matemáticas acudieron a los bancos de inversión ya los fondos de cobertura en aquella época porque pudieron idear algoritmos y métodos de datos novedosos.Dicho esto, la ciencia de los datos se está convirtiendo en una de las ocupaciones más adecuadas para el éxito en el siglo XXI. Se trata de una profesión informatizada, basada en la programación y de naturaleza analítica. Por lo tanto, no es de extrañar que la necesidad de científicos de datos haya preocupado en el mercado laboral en los últimos años.La oferta, en cambio, ha sido bastante restringida. Es un reto conseguir los conocimientos y habilidades necesarios para ser contratado como científico de datos .En este curso, las notaciones y matemáticas la jerga se reducen a lo más básico, cada tema se explica en un lenguaje sencillo, lo que facilita su comprensión. Una vez que tengas en tus manos el código, podrás jugar con él y construir sobre él. El énfasis de este curso está en entender y usar estos algoritmos en el mu

Atenção: nesse curso ainda estão sendo adicionadas aulas! Machine Learning (aprendizado de máquina) é uma área que representa uma evolução nos campos de Ciência da Computação, Análise de Dados, Engenharia de Software e Inteligência Artificial. Nesse curso você aprenderá Machine Learning com a linguagem de Programação Python. Não é preciso ter conhecimento em Python, pois o curso possui uma seção para quem é iniciante na linguagem. Além disso, o curso trata das principais bibliotecas para análise de dados e utilização de técnicas de aprendizado de máquina tais como Num Py, Pandas, Scikit-Learn e Matplotlib. Também serão explicadas técnicas de aprendizado de máquina para facilitar o entendimento e utilização das mesmas nos exemplos práticos. Todo o curso é 100% em vídeo-aulas, tem direito a certificado e acesso vitalício! Os instrutores Marcos Castro (mais de 12 mil alunos na Udemy) e Gileno Filho (mais de 10 mil alunos na Udemy) irão estar disponíveis para tirar quaisquer dúvidas através do fórum do curso. O que está esperando? Machine Learning é utilizado por empresas ao redor do mundo para facilitar a análise de dados. Vivemos a era do Big Data, o volume de dados produzidos é gigantesco e precisamos de técnicas para automatizar e nos ajudar a encontrar algum padrão nesses dados de forma que possamos resolver os problemas. Aguardamos você no curso!

Este curso básico de TensorFlow te enseñará a crear redes neuronales para Deep Learning o aprendizaje profundo.Es una guía fácil con muchos ejemplos, para entener las complejidades del marco de TensorFlow de Google.Este curso está repleto de ejemplos escritos en Python sobre Jupyter Notebook, para que puedas probarlos tu mismo.Estos son los temas tratados en este curso de TensorFlow :- Introduccion al Machine Learning- Instalacion del entorno de trabajo- Curso básico de Python sobre las librerías usadas en este curso: - Num Py - Pandas - Matplotlib - Scikit-Learn- Introducción a las redes neuronales (Deep Learning) - Neuronas y perceptrones - Funciones de activacion - Funciones de coste - Algoritmo del gradiente descendiente - Practicar con una red neuronal en el navegador- TensorFlow - Introducción a TensorFlow - Sintaxis básica de TensorFlow - Grafos en TensorFlow - Grafos por defecto - Variables y placeholders - Ejemplo de red neuronal - parte 1 - Ejemplo de red neuronal - parte 2 - Ejemplo de regresión simple con TensorFlow - Ejemplo de clasificación con TensorFlow - Ejemplo de regresión con TensorFlow - parte 1 - Ejemplo de regresión con TensorFlow - parte 2 - Ejemplo de regresión con TensorFlow - parte 3- Redes Neuronales Convolucionales - Introducción a las redes neuronales convolucionales - MNIST - Base de datos de imágenes de dígitos escritos a mano - Ejemplo con MNIST - Importar base de datos y mostrar una imagen- Redes Neuronales Recurrentes - Introducción a las redes neuronales recurrentes - Ejemplo de una red neuronal recurrente con TensorFlow - Ejemplo de series temporales - parte 1 - Ejemplo de series temporales - parte 2 - Ejemplo de series temporales - parte 3- Bibiliotecas - Estimator API - Ke

Neste curso, exploramos o vasto mundo de Data Science e Machine Learning, focando na base lógica e matemática por trás dos principais algoritmos utilizados na área. Veremos como funcionam os principais algoritmos de Regressão, Classificação, Clusterização, NLP, Deep Learning, Regras de Associação, Algoritmos Genéticos, Séries&nbsp;Temporais e muito mais - sem exagerar no "matematiquês". O curso foi pensado de forma a ser o mais democrático possível, servindo como porta de entrada para pessoas que queiram aprender de verdade os principais conceitos antes de entrar no mercado, pessoas que já estejam trabalhando com ciência de dados mas se veem com dificuldades de entender como funcionam os modelos, ou pessoas que simplesmente se interessam pela área e gostariam de aprender como funciona - não necessariamente visando adentrar o mercado. Até por isso, o curso não é tão orientado a código; ao invés de criar código para cada modelo e cada técnica mostrada, ao final do curso há uma seção com alguns projetos da vida real, em que podemos ver tanto como o código é feito, mas, principalmente, como é o raciocínio e as decisões tomadas para resolver problemas de dados.Também trago uma seção bastante rica e dedicada a explicar como se "produtizam" modelos em empresas, falando sobre coisas como deploy, monitoramento, construção de features, pré-processamento, definição de um projeto de ML, expectativa e visão do mercado, progressão de carreira e muito mais!O curso ainda tem um "crash course" de Python, opcional para quem já programa na linguagem, mas valiosa para aqueles que precisam de uma base mais sólida.

Dans ce cours, vous allez découvrir et approfondir les différents aspects liés à l'apprentissage automatique avec Python. Nous utiliserons les librairies telles que TensorFlow, Keras, Pandas, Numpy, Scikit-Learn, ...Les travaux sont accessibles et exploitables en ligne grâce à l'utilisation des carnets Jupyter avec Google Colab. Aucune installation de logiciel spécifique sur son ordinateur n'est requise car tout le travail se fait en ligne.A chaque étape d'apprentissage de ce cours, de nouveaux modèles sont introduits. Des explications claires permettent de bien les comprendre à travers 6 thèmes d'étude :Structure de base d'un réseau de neurones Reconnaissance d'image avec un réseau de neurones à convolution 2DTraitement d'image avec un réseau de neurones profond à convolution 2DSystèmes de recommandations et d'analyse des ressentisDétection d'anomalies dans les données Analyse et prédiction sur les séries temporelles Les activités en Python expliquent clairement comment les exploiter. Des exercices sont régulièrement proposés pour consolider votre apprentissage.D'une durée totale de 19,5 heures, ce cours vous permettra d'être à l'aise avec les outils actuels du Deep Learning. Vous serez alors capable d'utiliser ces ressources pour créer vos propres projets et d'approfondir avec sérénité et en autonomie vos connaissances dans ce domaine.=== Prérequis ===Vous n'avez pas besoin d'être un spécialiste du langage Python. En effet, au fur et à mesure de votre progression, vous manierez ce langage et découvrirez les subtilités liées à son utilisation.Si vous êtes complètement débutant en Deep Learning, alors ce cours est fait pour vous. Ce cours est structuré de manière progressive pour acquérir petit à petit les bases du de

En este curso se enseñan todos los conocimientos necesarios para convertirse en un Data Scientist (Científico de Datos). Para ello usaremos el lenguaje de Programación Python como herramienta, ya que es uno de los lenguajes con más demanda hoy en dia.En concreto, se tratarán en profundidad los siguientes apartados:- Programación en Python, donde aprendemos a programar en uno de los lenguajes más populares hoy en día como es Python.- Análisis de Datos, donde aprenderemos como realizar un Análisis Exploratorio de Datos, usando técnicas estadísticas y de Visualización de Datos.- Machine Learning, donde aprenderemos como crear modelos predictivos, evaluarlos y usarlos en un entorno de desarrollo.- Deep Learning, donde nos enfocamos en la creación de Redes Neuronales.- Web Scraping, donde aprenderemos técnicas para extraer información de páginas web.- Big Data, donde aprenderemos a como procesar datasets de gran tamaño asi como entrenar modelos predictivos con ellos.

A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina).A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando a biblioteca PyTorch o Python! Este curso apresenta desde os conceitos mais básicos sobre as redes neurais até técnicas mais modernas e avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Para isso, o conteúdo está dividido em sete partes: redes neurais artificiais, redes neurais convolucionais, autoencoders, redes adversariais generativas (GANs)</strong

Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LL Ms like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with

Common scenario: You try to get into machine learning and data science, but there's SO&nbsp;MUCH&nbsp;MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Calculus is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.Are you ready?Let's go!Suggested prerequisites:Firm understanding of high school math (functions, algebra, trigonometry)

A-Z™ | TensorFlow ile Derin Öğrenme Kursumuzda klasik ve derin öğrenme tabanlı yöntemlerini kullanarak sınıflandırma nasıl yapıldığını öğrenip, TensorFlow kütüphaneleriyle gerçek hayat projeleri yapacağız.Projelerle Yapay Zeka ve Bilgisayarlı Görü Kursu İçeriği Giriş BölümüDerin Öğrenme Teori Derin Öğrenme Nedir Yapay Sinir AğlarıAktivasyon FonksiyonlarıOptimizasyon AlgoritmalarıLoss (Kayıp) FonksiyonlarıDerin Öğrenme TeoriCNN (Convolutional Neural Networks) Teori Evrişim İşlemiCNN (Convolutional Neural Networks)Piksel Ekleme (Padding)Adım Kaydırma (Stride)Ortaklama (Pooling)Ek Teori Epoch ve Batch Size Dropout Early Stopping Learning Rate TensorFlow ile Derin Öğrenme TensorFlow Temelleri Veriyi Hazırlama Model Oluşumu Sequential Model Egitimi Model Testi | 1. Kısım Model Testi | 2. Kısım Modeli Kaydetme/Yükleme - Save/Load Model Sonuçlarını Görselleştirme Modelin Ara Katmalarını Görselleştirme Functional Bir Model Oluşturma Callbacks | 1. kısım Callbacks | 2. kısım Data Augmentation - Veri Arttırma | 1. Kısım Data Augmentation - Veri Arttırma | 2. Kısım Transfer Learning - VGG Hazır Model Kullanma - VGG TensorFlow ile Trafik İşaretlerini Sınıflandırma Veriyi Hazırlama Model Eğitimi ve Test Real Time'da Test TensorFlow'da Weights &amp; Biases (WandB) | Özel Veri Wandb ile Keras'da Temel Fonsiyonlar Wandb ile Keras'da Sweepler Wandb ile Keras'da Sweep - Bonus Video TensorFlow Lite - Android App - Object detection - İmage Classification Efficient Det Lite Model Eğitimi - Object detection Efficient Det Lite Modeli Android'de Çalıştırma 1 - Object detection Efficient Det Lite Modeli Androi

A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios&nbsp;computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas&nbsp;técnicas, tais como:&nbsp;carros autônomos, descoberta de novos medicamentos,&nbsp;cura e diagnóstico antecipado de&nbsp;doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, e Bay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning&nbsp;é atualmente um dos campos de trabalho&nbsp;mais relevantes&nbsp;da Inteligência Artificial, sendo que o&nbsp;mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando o Tensor

"Python TOTAL", el curso Best-Seller que ha enseñado Python desde cero a miles y miles, necesitaba un complemento perfecto:&nbsp;"Python TOTAL&nbsp;para Data Science y Machine Learning".¿Por qué hacía falta?Porque con este curso, además de aprender Python desde cero, podrás llevarlo hacia la ciencia del momento:&nbsp;Data Science (o Ciencias de la Información), para poder programar herramientas capaces de procesar cantidades monumentales de información, y de generar no solo visualizaciones relevantes, informativas y atractivas, sino también predicciones a partir de los datos que disponemos.Con "Python Total para Data science &amp; Machine Learning" podrás ayudar a quienes toman decisiones a entender mejor el contexto y la realidad sobre la cual están operando, para poder ser eficaces, eficientes y acertivos en sus decisiones.¿Que encontrarás en este curso?18 días de aprendizaje intenso y práctico Cientos de ejercicios de código en la plataforma (3 por cada lección)Vientos de archivos de código descargable Proyectos díarios del mundo real para aplicar lo aprendido Decenas de bases de datos para prácticas Cuestionarios Lecciones teóricas y prácticas hechas con amor por la simplicidad¿Qué temas cubre este curso?Python básico Pandas Num PyMatplotlib Seaborn Scikit-Learn TensorFlow Machine Learning Excel y Power BI para Data Science Algoritmos de Aprendizaje Supervisado, No Supervisado y por Reforzamiento Bases de DatosAP Is Deep Learning Etica y Provacidad en Data Sciencey muchísimo más<

Selamat datang di program Pelatihan Data Science dan Machine Learning Dengan R!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan R.Bagi rekan - rekan yang belum menguasai pemrograman R, pelatihan juga memberikan konten pemrograman dasar untuk&nbsp; Rsehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman R, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanakan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihan Pemrograman RPengenalan tool dan editor seperti RStudio, Jupyter Notebook / Jupyter Lab, Jupyter / Notebook Dengan Anaconda, dan Google Colab Visualisasi Data Visualisasi Data dengan ggplot2Dataset, Pra-Proses dan Pengurangan Dimensi Feature Manipulasi dan Analisa data Eksplorasi data science dan machine learning Permasalahan dan Penyelesaian Kasus Linear Regression Permasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Ensemble Methods Hyperparameter Tuning Untuk Model Machine Learning Kumpulan Studi Kasus Jika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan ikut terlibat diskusinya.

Cette formation est conçue pour&nbsp; donner une compréhension complète de la data science, avec un focus particulier sur l’utilisation du langage R, un des outils les plus performants pour l’analyse statistique et la visualisation de données. Voici ce que vous apprendrez au cours de cette formation :Les bases de R et de la data science : Nous commencerons par les bases du langage R, afin que vous puissiez maîtriser les outils essentiels de manipulation et d’analyse de données.Visualisation des données : L’une des compétences les plus recherchées aujourd’hui est la capacité à visualiser des données de manière claire et percutante. Nous apprendrons ensemble à utiliser des bibliothèques comme ggplot2 pour créer des graphiques informatifs.Nettoyage et préparation des données : Une partie essentielle de l’analyse de données consiste à préparer les jeux de données. Vous apprendrez à manipuler, transformer et nettoyer des données brutes pour les rendre exploitables.Algorithmes de machine learning : algorithmes d'apprentissage supervisé et non supervisé en montrant comment créer des modèles prédictifs pour résoudre des problèmes réels.Applications concrètes et projets : Tout au long de la formation, vous aurez l’occasion de travailler sur des exemples et des études de cas, afin de renforcer vos compétences et de vous préparer à intégrer le monde professionnel de la data science.

Fundamentos da linguagem de programação Python , que é a principal base de linguagem para a aplicação da ciência de dados Estudo das principais funcionalidades da biblioteca Pandas , que é a principal biblioteca de manipulação de dados da Data Science Estudo das principais funcionalidades da biblioteca Numpy , que é a principal biblioteca de manipulação de operações matemáticas Estudo das principais bibliotecas de Visualização de Dados : Matplotlib e Seaborn Manipulando Time Series, que são os tipos usados em ​​datas e horas Redução de Dimensões com PCA e TSNE Estatística para Data Science.Machine Learning , com teoria e aplicação prática de estratégias básicas e avançadas Intuição e aplicação dos seguintes modelos preditivos:Linear_Regression (Regressão Linear) Logistic_Regression (Regressão Lógica)Decision_Tree&nbsp;(Árvore de Decisão)Random_Forest (Floresta Aleatória)Stochastic_Gradient_Descent (SGD)Support_Vector_Machine (SVM) Ada Boost Gradient_Boost (Impulsionamento Gradiente)K-Means_Clustering - (K-Médias de Grupos)K-Nearest_Neighbors (KNN) PROJETO: Predição da Idade dos Passageiros do Titanic (Regressão Linear)PROJETO: Classificação de sobrevivência dos passageiros do Titanic (Classificação)PROJETO: Análise de Sentimentos de Frases do Twitter (Processamento de Linguagem Natural - PLN)PROJETO: Funcionamento e uso do modelo de detecção e classificação de objetos em imagens e vídeos YOLO (Visão Computacional)PROJETO:&nbsp;Segregando Clientes por Padrões de

Datascience;&nbsp;machine learning, data science, python, statistics, statistics, r, machine learning python, deep learning, python programming, django Hello there,Welcome to “ Kaggle - Get Best Profile in Data Science &amp; Machine Learning ” course.Kaggle is Machine Learning &amp; Data Science community. Boost your CV in Data Science, Machine Learning, Python with Kaggle Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, Oak Academy has a course to help you apply machine learning to your work. It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the i Phone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models.Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GP Us and a huge repository of community-published data &amp; code.Kaggle is

Dieser Kurs ist dein umfassender Einstieg in die Welt des Deep Learnings – mit einem klaren Fokus auf Praxis, fundierter Theorie und moderner Python-Entwicklung mit TensorFlow 2 und Keras.Statt nur Code-Schnipsel zu kopieren, lernst du wirklich zu verstehen, wie neuronale Netze funktionieren – von der mathematischen Basis bis zur Anwendung. Du wirst eigene Modelle Schritt für Schritt selbst aufbauen und trainieren, Bilddaten analysieren und sogar Texte mit KI verarbeiten.Du startest mit den Grundlagen des Machine Learning und neuronaler Netzwerke – und steigst dann tief in die wichtigsten Netzarchitekturen ein: Von klassischen Fully Connected Networks über CNNs für Bildverarbeitung bis zu RNNs/LSTMs für Zeitreihen und Texte. Dabei kommen State-of-the-art Modelle wie Res Net und Dense Net ebenfalls nicht zu kurz.Auch Natural Language Processing (NLP) ist Teil des Kurses – perfekt, um moderne KI-Anwendungen wie Chatbots oder Textklassifizierer zu entwickeln.Kursinhalte im Überblick:Einführung in Machine Learning und neuronale Netze Mathematische Grundlagen (z. B. Aktivierungsfunktionen, Backpropagation)Eigene Modelle in TensorFlow 2 und Keras entwickeln Visualisierung und Debugging mit Tensor Board Digitale Bildverarbeitung mit CNNs Moderne Architekturen: Res Net, Dense Net Sequenzmodelle: RNNs und LSTMs für zeitabhängige Daten Einstieg in Natural Language Processing (NLP) mit Keras Praxisnahe Projekte und Übungen Ziel:Werde fit im Umgang mit modernen KI-Technologien und baue deine eigenen Deep-Learning-Modelle – fundiert, praxisnah, professionell.<p

This professional certificate from IBM teaches how to build AI-powered applications using IBM Watson. The program covers natural language processing, computer vision, and building AI-powered chatbots, with a focus on practical application.