Hands-On Machine Learning with Scikit-Learn and PyTorch is a practical ML and deep learning guide. Scikit-Learn for traditional ML algorithms Deep neural networks with PyTorch Computer vision and NLP projects...

Mathematics for Machine Learning is a comprehensive resource for mastering machine learning, with clear explanations and practical guidance. Core machine learning concepts and theory Practical examples and case studies Best...

Grokking Machine Learning teaches core ML algorithms through clear visual explanations and coding exercises. Logistic regression, trees, and SVMs Neural networks and backpropagation Clustering and dimensionality reduction For beginners entering...

Designing Machine Learning Systems by Chip Huyen covers the full ML system design lifecycle. Data collection and feature engineering Model training, evaluation, and selection Deployment, monitoring, and continual learning Essential...

The Hundred-Page Machine Learning Book by Burkov delivers compact ML coverage. Supervised and unsupervised learning SVM, neural networks, and ensembles Practical ML tips and best practices Great for practitioners wanting...

An Introduction to Statistical Learning with Applications in Python is an accessible ML methods textbook. Regression, classification, and resampling Tree-based methods and boosting Unsupervised learning and clustering Python labs for...

Probabilistic Machine Learning: An Introduction by Kevin Murphy is a rigorous probabilistic ML textbook. Bayesian reasoning and probabilistic models Linear models, trees, and kernels Deep learning and neural networks For...

Machine Learning Design Patterns identifies solutions to common ML systems design challenges. 30+ ML design patterns with examples Feature engineering and data patterns Model serving and reproducibility For data scientists...

AI and Machine Learning for Coders by Laurence Moroney is hands-on for developers entering AI/ML. Neural networks in TensorFlow Computer vision with CNNs NLP and sequence modeling For developers transitioning...

The StatQuest Illustrated Guide To Machine Learning brings visual teaching to ML concepts. Linear models, trees, and ensemble methods Neural networks and deep learning basics Statistics and probability for ML...

Reinforcement Learning: An Introduction by Sutton and Barto is the foundational RL textbook. MDPs and dynamic programming Temporal difference and Q-learning Policy gradient and actor-critic methods Essential reference for RL...

Machine Learning With PyTorch and Scikit-Learn is a comprehensive ML guide using Python's top libraries. Supervised and unsupervised ML algorithms Deep neural networks with PyTorch Model evaluation and deployment From...