You're reading from Practical Machine Learning Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials

Product type Paperback

Published in Jan 2016

Publisher Packt

ISBN-13 9781784399689

Length 468 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Sunila Gollapudi

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Table of Contents (16) Chapters

Preface

1. Introduction to Machine learning FREE CHAPTER

2. Machine learning and Large-scale datasets

3. An Introduction to Hadoop's Architecture and Ecosystem

4. Machine Learning Tools, Libraries, and Frameworks

5. Decision Tree based learning

6. Instance and Kernel Methods Based Learning

7. Association Rules based learning

8. Clustering based learning

9. Bayesian learning

10. Regression based learning

11. Deep learning

12. Reinforcement learning

13. Ensemble learning

14. New generation data architectures for Machine learning

Index

Machine learning solution architecture for big data (employing Hadoop)

In this section, let us look at the essential architecture components for implementing a Machine learning solution considering big data requirements.

The proposed solution architecture should support the consumption of a variety of data sources in an efficient and cost-effective way. The following figure summarizes the core architecture components that should potentially be a part of the Machine learning solution technology stack. The choice of frameworks can either be open source or packaged license options. In the context of this book, we consider the latest version of open source (Apache) distribution of Hadoop and its ecosystem components.