Launch, Monitor, and Maintain Your System.

Analyze the Best Models and Their Errors.Better Evaluation Using Cross-Validation.Training and Evaluating on the Training Set.Handling Text and Categorical Attributes.Prepare the Data for Machine Learning Algorithms.Experimenting with Attribute Combinations.Discover and Visualize the Data to Gain Insights.Take a Quick Look at the Data Structure.Hyperparameter Tuning and Model Selection.

Instance-Based Versus Model-Based Learning.

Learn techniques for training and scaling deep neural nets.

Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning.

Use the TensorFlow library to build and train neural nets.

Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods.

Use Scikit-Learn to track an example machine-learning project end-to-end.

Explore the machine learning landscape, particularly neural nets.

With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. This practical book shows you how.īy using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning.

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