List of important books:
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper
- Neural Networks and Deep Learning by Michael Nielsen
- Automate the Boring Stuff with Python by Al Sweigart
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- Learning Deep Architectures for AI by Yoshua Bengio
- Python for Data Science Handbook by Jake VanderPlas
- The Hundred-Page Machine Learning Book by Andriy Burkov.
- A Course in Machine Learning by Hal Daumé III
- Intuitive ML and Big Data in C++, Scala, Java, and Python by Kareem Alkaseer
- Python Notes for Professionals book
- Learning Pandas
- Machine Learning - A First Course for Engineers and Scientists by Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön
- Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
- A Comprehensive Guide to Machine Learning Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, Jennifer Listgarten, Anant Sahai
- Data Mining and Analysis by Mohammed J. Zaki and Wagner Meira Jr.
- SQL Notes for Professionals book
- The Data Science Handbook by Carl Shan, Henry Wang, William Chen, and Max Song
- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
- Algorithms Notes for Professionals book
- Bayesian Reasoning and Machine Learning by David Barber
- 800+ Q&As about: Stats, Python, ML, DL, NLP, CV, MLOps by Steve Nouri
- An Introduction/A History of Data Science
- Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan
- Few more coming soon.
Courtesy: Steve Nouri
Content: Free Data Science Books (20+)