Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3e

Through a series of recent breakthroughs, deep learning has advanced the entire field of machine learning. Now, even programmers with little knowledge of this technology can use simple, efficient tools to implement programs that can learn from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to provide you with an intuitive understanding of the concepts and tools for building intelligent systems.

In this updated third edition, author Aurélien Géron explores a variety of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you learn. Programming experience is all you need to get started.

• Use Scikit-learn to follow a machine learning example project from start to finish.
• Explore various models, including support vector machines, decision trees, random forests, and ensemble methods.
• Utilize unsupervised learning with techniques such as dimensionality reduction, clustering, and anomaly detection.
• Dive into neural network architectures, including convolutional networks, recurrent networks, generative adversarial networks, autoencoders, diffusion models, and transformers.
• Use TensorFlow and Keras to create and train neural networks for computer vision, natural language processing, generative models, and deep reinforcement learning.