By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. \nn \n

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• • Explore the machine learning landscape, particularly neural nets
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  • Use Scikit-Learn to track an example machine-learning project end-to-end
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  • Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
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• • Use the Tensor Flow library to build and train neural nets
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• • Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
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• • Learn techniques for training and scaling deep neural nets.
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