Free Books for Data Science and AI Enthusiasts

In my journey as a data science enthusiast, I’ve come across a treasure trove of valuable resources. Here’s a list of fantastic books that you can explore for free. These are meant solely for personal use. If you find any of these resources valuable, I highly encourage you to buy the books or consider donating to support the authors. Let’s continue learning and also show appreciation for the creators! 🙏

Free Books for Data Science and AI Enthusiasts 

By Shaikh Minhaj

Data Science & Analysis

1. Data Science at the Command Line by Jeroen Janssens
2. Learning Pandas
3. Pandas: Powerful Python Data Analysis Toolkit by Wes McKinney and the Pandas Development Team
4. Data Mining and Analysis by Mohammed J. Zaki and Wagner Meira Jr.

Machine Learning (ML)

1. Hands-on Machine Learning with Scikit-learn, Keras, and TensorFlow by Aurélien Géron
2. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
3. Machine Learning: A First Course for Engineers and Scientists by Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön
4. A Comprehensive Guide to Machine Learning by Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, Jennifer Listgarten, and Anant Sahai
5. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
6. Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan
7. Patterns, Predictions, and Actions: A Story About Machine Learning by Moritz Hardt and Benjamin Recht

Deep Learning (DL) & Neural Networks

1. Deep Learning on Graphs by Yao Ma and Jiliang Tang
2. Learning Deep Architectures for AI by Yoshua Bengio
3. Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola
4. The Hundred-Page Machine Learning Book by Andriy Burkov
5. A Course in Machine Learning by Hal Daumé III
6. Neural Networks and Deep Learning by Michael Nielsen
7. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Python for Data Science & Programming

1. Python for Data Science Handbook by Jake VanderPlas
2. Python Notes for Professionals Book
3. Learn Python the Right Way by Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers
4. Automate the Boring Stuff with Python by Al Sweigart

Statistics & Probability

1. Practical Statistics for Data Science by Peter Bruce & Andrew Bruce
2. Bayesian Reasoning and Machine Learning by David Barber

SQL & Databases

1. SQL Notes for Professionals Book

Natural Language Processing (NLP)

1. Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

Artificial Intelligence (AI) & MLOps

1. Intuitive ML and Big Data in C++, Scala, Java, and Python by Kareem Alkaseer
2. Introducing MLOps by Mark Treveil, Nicolas Omont, Clement Stenac, Kenji Lefevre, Du Phan
3. Deep Learning Interviews: Hundreds of Fully Solved Job Interview Questions from a Wide Range of Key Topics in AI by Shlomo Kashani, Amir Ivry

Excel & Data Visualization

1. Advanced Excel by Towson University

General Data Science

1. The Data Science Handbook by Carl Shan, Henry Wang, William Chen, and Max Song
2. An Introduction/A History of Data Science

Kubernetes & Deployment

1. Kubernetes Up and Running by Brendan Burns, Joe Beda & Kelsey Hightower

Algorithms

1. Algorithms Notes for Professionals Book

Support the Authors & Creators 💡

Many of these resources are free for educational purposes, but that doesn't mean they come without hard work and dedication. If you find these resources valuable, please show your support by purchasing official versions of the books, donating to creators, or spreading the word. Together, we can help keep the knowledge-sharing spirit alive!