Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Aurélien Géron’s bestselling guide, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow," offers a practical introduction to building intelligent systems. Now in its third edition, the book is designed for programmers with little to no prior knowledge of machine learning, utilizing concrete examples and minimal theory to foster an intuitive understanding of complex concepts. By leveraging production-ready Python frameworks, readers learn to implement programs capable of learning from data, ranging from simple linear regression to advanced deep neural networks. The text emphasizes hands-on application, providing numerous code examples and exercises to reinforce learning. The book covers a comprehensive array of machine learning techniques and models. Readers will learn to use Scikit-learn to manage an ML project from start to finish, exploring support vector machines, decision trees, random forests, and ensemble methods. It also delves into unsupervised learning strategies like dimensionality reduction, clustering, and anomaly detection. Significant attention is given to deep learning architectures, including convolutional nets, recurrent nets, generative adversarial networks (GANs), autoencoders, diffusion models, and transformers. Beyond basic modeling, the text guides readers through using TensorFlow and Keras to build and train neural networks for diverse applications. These include computer vision, natural language processing (NLP), generative models, and deep reinforcement learning. Whether the goal is to give a robot a brain, recognize faces, or analyze vast amounts of corporate data—such as user logs, financial records, or sensor data—this resource provides the necessary tools. It empowers developers to unearth hidden insights and solve practical problems using modern machine learning technologies.