Welcome to ML_books! This repository aims to be a curated collection of links to some of the most famous and freely available books on Machine Learning (ML) that are online. Whether you're a beginner or an advanced practitioner, you'll find resources here to guide you on your journey through the world of ML.
We have gathered some of the best books covering a range of topics in Machine Learning, from foundational concepts to cutting-edge research. Here is a list of the available books:
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Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- A comprehensive introduction to deep learning and neural networks.
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The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- An in-depth resource covering the theoretical foundations of statistical learning methods.
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Pattern Recognition and Machine Learning by Christopher M. Bishop
- One of the most popular and accessible introductions to machine learning.
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Bayesian Reasoning and Machine Learning by David Barber
- Focuses on probabilistic graphical models and Bayesian methods.
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Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
- An in-depth look at probabilistic models for machine learning.
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Introduction to Machine Learning with Python by Andreas Müller and Sarah Guido
- A practical guide to implementing machine learning algorithms using Python.
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Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
- The definitive guide to reinforcement learning.
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An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- A more accessible version of "The Elements of Statistical Learning," perfect for beginners.
You can use this repository to quickly access high-quality ML books online. Each link will direct you to the official website or a free downloadable version of the book.
- Cloning the repository:
Clone this repository using the following command:git clone https://github.com/yourusername/ML_books.git