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

Through a recent series of breakthroughs, deep learning has boosted all the 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) that can assist you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this up to date third edition, writer Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. A large number of code examples and exercises all over the book assist you to apply what you’ve learned. Programming experience is all you wish to have to get started.

• Use Scikit-learn how to track an example ML project end to end
• Explore several models, including reinforce 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