- The AI Expedition: From Basics to Brilliance (Manas Joshi)
- Approaching (Almost) Any Machine Learning Problem
- Foundations of Machine Learning (Mehryar Mohri, et al)
- Machine Learning Yearning (Andrew Ng)
- Dive into Deep Learning (Aston Zhang, et al.)
- Reinforcement Learning: An Introduction, Second Edition
- Distributional Reinforcement Learning (Marc G. Bellemare, et al)
- Algorithms for Reinforcement Learning (Csaba Szepesvari)
- An Introduction to Deep Reinforcement Learning
- Understanding Machine Learning: From Theory to Algorithms
- Machine Learning from Scratch (Danny Friedman)
- Deep Learning with Python, 2nd Edition (Francois Chollet)
- Deep Learning for Coders with Fastai and PyTorch
- Deep Learning with PyTorch (Eli Stevens, et al.)
- Probabilistic Machine Learning: An Introduction (Kevin Murphy)
- Introduction to Statistical Learning: with Applications in Python (Gareth James, et al)
- Deep Learning (Ian Goodfellow, et al)
- The Amazing Journey of Reason: from DNA to Artificial Intelligence
- Machine Learning with Neural Networks (Bernhard Mehlig)
- Efficient Learning Machines: Theories, Concepts, and Applications
- Boosting: Foundations and Algorithms (Robert E. Schapire, et al)
- An Introduction to Machine Learning Interpretability
- Pen and Paper Exercises in Machine Learning (Michael Gutmann)
- Interpretable Machine Learning: Black Box Models Explainable
- Pattern Recognition and Machine Learning (Christopher Bishop)
- Gaussian Processes for Machine Learning (Carl E. Rasmussen)
- The Hundred-Page Machine Learning Book (Andriy Burkov)
- An Introduction to Statistical Learning (Gareth James, et al)
- Machine Learning Engineering (Andriy Burkov)
- Machine Learning from Scratch (Danny Friedman)
- An Introduction to Quantum Machine Learning for Engineers