Through a recent series of breakthroughs deep learning has boosted the entire field of machine
learning. Now even programmers who know close to nothing about this technology can use simple
efficient tools to implement programs capable of learning from data. This bestselling book uses
concrete examples minimal theory and production-ready Python frameworks (Scikit-Learn Keras
and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for
building intelligent systems. With this updated third edition author Aurélien Géron explores a
range 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've
learned. Programming experience is all you need to get started. Use Scikit-learn to track an
example ML project end to end Explore several models including support vector machines
decision trees random forests and ensemble methods Exploit unsupervised learning techniques
such as dimensionality reduction clustering and anomaly detection Dive into neural net
architectures including convolutional nets recurrent nets generative adversarial networks
autoencoders diffusion models and transformers Use TensorFlow and Keras to build and train
neural nets for computer vision natural language processing generative models and deep
reinforcement learning
