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About this Book

You may be wondering—is this a book for me?

If you are seeking a textbook on machine learning, no. This book does not attempt to fully explain the theory and derivation of the various algorithms and techniques presented here. Some familiarity with machine learning techniques and related concepts, like matrix and vector math, is useful in reading this book, but not assumed.

If you are developing modern, intelligent applications, then the answer is, yes. This book provides a practical rather than a theoretical treatment of these techniques, along with complete examples and recipes for solutions. It develops some insights gleaned by experienced practitioners in the course of demonstrating how Mahout can be deployed to solve problems.

If you are a researcher in artificial intelligence, machine learning, and related areas—yes. Chances are your biggest obstacle is translating new algorithms into practice. Mahout provides a fertile framework and collection of patterns and ready-made components for testing and deploying new large-scale algorithms. This book is an express ticket to deploying machine learning systems on top of complex distributed computing frameworks.

Roadmap

Code conventions and downloads

Multimedia extras

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