A curated collection of essential books for Data Science, Machine Learning, and Deep Learning enthusiasts. Whether you are a beginner taking your first steps or an experienced practitioner looking to deepen your theoretical understanding, you will find valuable resources here.
Machine learning and Data Science are rapidly evolving fields, and having the right foundational knowledge is crucial. This repository serves as a centralized archive of highly recommended and widely respected books in the domain, available for offline reference and study.
The following books are available in this repository as compressed archives. Click on the links to access the files directly.
| # | Title | Author(s) | Focus Area | Link |
|---|---|---|---|---|
| 1 | Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Ed.) | Aurélien Géron | Practical ML, TensorFlow, Keras | Download |
| 2 | Deep Learning with Python | François Chollet | Deep Learning, Keras | Download |
| 3 | Deep Learning | Ian Goodfellow, Yoshua Bengio, Aaron Courville | Theoretical Deep Learning | Download |
| 4 | Pattern Recognition and Machine Learning | Christopher Bishop | Theoretical ML, Probabilistic Models | Download |
| 5 | Introduction to Statistical Learning (ISLR) | Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani | Statistical Modeling, ML Basics | Download |
| 6 | Python Machine Learning | Sebastian Raschka, Vahid Mirjalili | Practical ML, Python | Download |
| 7 | Understanding Machine Learning: From Theory to Algorithms | Shai Shalev-Shwartz, Shai Ben-David | ML Theory | Download |
| 8 | Python Data Science Handbook | Jake VanderPlas | Pandas, NumPy, Matplotlib | Download |
| 9 | Numpy for Quantitative Finance | Various | Quantitative Finance, NumPy | Download |
| 10 | Python Crash Course | Eric Matthes | Python Basics | Download |
| 11 | Pandas Guide | Various | Data Manipulation, Pandas | Download |
| 12 | Natural Language Toolkit (NLTK) | Various | NLP, Python | Download |
| 13 | Hands-on ML (1st Ed.) | Aurélien Géron | Practical ML | Download |
- Choose your path: Select a book from the collection based on your current skill level and learning goals.
- Beginners: Start with Python Crash Course or Python Data Science Handbook.
- Practical ML: Dive into Hands-On Machine Learning.
- Theory: Explore Pattern Recognition and Machine Learning or Deep Learning.
- Extract the contents: Download the corresponding
.zipfile and extract it on your local machine. - Practice: Read through the chapters and try to implement the examples in your own Python environment.
- Engage: Join online Data Science and Machine Learning communities to discuss concepts and share your progress!
We welcome contributions! If you have a highly recommended book or resource that should be added to this list, feel free to open a pull request or an issue.
Please ensure that any added materials comply with copyright and distribution guidelines.