Welcome to the repository dedicated to summarizing and taking notes on the book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. This repository aims to provide a concise and organized overview of key concepts, code snippets, and practical insights from the book.
"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" is a widely acclaimed resource that takes a practical and hands-on approach to understanding the fundamentals of machine learning and deep learning. Authored by Aurélien Géron, this book has become a go-to reference for both beginners and experienced practitioners in the field.
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Chapters:
- Each chapter of the book has its dedicated directory, containing notes, summaries, and relevant code examples.
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Code Snippets:
- Find key code snippets and examples presented in the book within the "code" directory.
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Resources:
Feel free to navigate through the directories based on the chapters you are interested in. Each chapter's notes and code snippets aim to provide a quick reference, summary, and practical implementation of the concepts discussed in the book.
- Clone the repository:
git clone https://github.com/Islam-hady9/Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow-Notes.git - Navigate to the desired chapter directory.
- Explore the notes, summaries, and code snippets.
Happy learning and exploring the exciting world of machine learning with "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow"!
- Part I. The Fundamentals of Machine Learning
- Chapter 1: The Machine Learning Landscape
- Chapter 2: End-to-End Machine Learning Project
- Chapter 3: Classification
- Chapter 4: Training Models
- Chapter 5: Support Vector Machines
- Chapter 6: Decision Trees
- Chapter 7: Ensemble Learning and Random Forests
- Chapter 8: Dimensionality Reduction
- Chapter 9: Unsupervised Learning Techniques
- Part II. Neural Networks and Deep Learning
- Chapter 10: Introduction to Artificial Neural Networks with Keras
- Chapter 11: Training Deep Neural Networks
- Chapter 12: Custom Models and Training with TensorFlow
- Chapter 13: Loading and Preprocessing Data with TensorFlow
- Chapter 14: Deep Computer Vision Using Convolutional Neural Networks
- Chapter 15: Processing Sequences Using RNNs and CNNs
- Chapter 16: Natural Language Processing with RNNs and Attention
- Chapter 17: Autoencoders, GANs, and Diffusion Models
- Chapter 18: Reinforcement Learning
- Chapter 19: Training and Deploying TensorFlow Models at Scale
- Machine learning is the science (and art) of programming computers so they can learn from data.
- [Machine learning is the] field of study that gives computers the ability to learn without being explicitly programmed. —Arthur Samuel, 1959
- A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. —Tom Mitchell, 1997
To summarize, machine learning is great for:
- Problems for which existing solutions require a lot of finetuning or long lists of rules (a machine learning model can often simplify code and perform better than the traditional approach)
- Complex problems for which using a traditional approach yields no good solution (the best machine learning techniques can perhaps find a solution)
- Fluctuating environments (a machine learning system can easily be retrained on new data, always keeping it up to date)
- Getting insights about complex problems and large amounts of data.
- Analyzing images of products on a production line to automatically classify them.
- Detecting tumors in brain scans.
- Automatically classifying news articles.
- Automatically flagging offensive comments on discussion forums.
- Summarizing long documents automatically.
- Creating a chatbot or a personal assistant.
- Forecasting your company’s revenue next year, based on many performance metrics.
- Making your app react to voice commands.
- Detecting credit card fraud.
- Segmenting clients based on their purchases so that you can design a different marketing strategy for each segment.
- Representing a complex, high-dimensional dataset in a clear and insightful diagram.
- Recommending a product that a client may be interested in, based on past purchases.
- Building an intelligent bot for a game.
There are so many different types of machine learning systems that it is useful to classify them in broad categories, based on the following criteria:
- How they are supervised during training (supervised, unsupervised, semi-supervised, self-supervised, and others)
- Whether or not they can learn incrementally on the fly (online versus batch learning)
- Whether they work by simply comparing new data points to known data points, or instead by detecting patterns in the training data and building a predictive model, much like scientists do (instance-based versus model-based learning)