"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron provides practical implementation examples using popular frameworks. The book balances theory with code examples and includes real-world applications. It's particularly useful for developers who want to move from theory to building actual machine learning systems.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville offers comprehensive coverage of deep learning concepts. While more theoretical than practical, it explains complex ideas clearly and serves as an excellent reference. The book covers neural networks, optimization algorithms, and modern architectures used in current AI systems.
"Introduction to Machine Learning with Python" by Andreas Müller and Sarah Guido focuses on practical applications using scikit-learn. It teaches core concepts through coding examples and real datasets. The book is especially good for Python developers who want to understand machine learning algorithms and when to use them. It includes sections on model evaluation, feature engineering, and pipeline building.