What are some good books on machine learning?

Here are some recommended books on machine learning, tailored for developers and technical professionals seeking both practical guidance and foundational knowledge:

Practical Guides for Hands-On Learning A strong starting point is “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron. This book emphasizes coding and real-world projects, using Python frameworks like Scikit-Learn for traditional algorithms (e.g., linear regression, decision trees) and TensorFlow/Keras for neural networks. Each chapter includes exercises, making it ideal for developers who learn by building. For example, it walks through creating image classifiers with CNNs and deploying models using TensorFlow Serving. Another practical resource is “Python Machine Learning” by Sebastian Raschka and Vahid Mirjalili, which covers modern libraries like PyTorch and XGBoost, with clear explanations of evaluation metrics and hyperparameter tuning.

Foundational Theory and Statistical Insights To deepen theoretical understanding, “The Elements of Statistical Learning” by Hastie, Tibshirani, and Friedman is a classic. It explains core concepts like regularization, SVMs, and ensemble methods with mathematical rigor, though it assumes familiarity with linear algebra and calculus. For a Bayesian perspective, “Pattern Recognition and Machine Learning” by Christopher Bishop balances theory and intuition, covering topics like Gaussian processes and variational inference. These books are best for developers transitioning to research roles or aiming to grasp the “why” behind algorithms. For a lighter theoretical introduction, “Understanding Machine Learning” by Shalev-Shwartz and Ben-David offers formal proofs with minimal prerequisites.

Specialized Topics and Advanced Techniques For those focusing on neural networks, “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville remains a definitive guide. It explains architectures like RNNs and GANs while addressing challenges like optimization and regularization. Developers interested in probabilistic models should consider “Machine Learning: A Probabilistic Perspective” by Kevin Murphy, which details graphical models, clustering, and reinforcement learning using probability theory. These books suit developers tackling specialized problems, such as natural language processing or uncertainty estimation, and provide code snippets in Python or R to illustrate concepts.

Choose books based on your goals: start with hands-on guides to build projects, then explore theory to refine your approach, and finally dive into specialized texts as your needs evolve.