Book Image

Practical Machine Learning

By : Sunila Gollapudi
Book Image

Practical Machine Learning

By: Sunila Gollapudi

Overview of this book

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.
Table of Contents (23 chapters)
Practical Machine Learning
About the Author
About the Reviewers

Apache Mahout

Apache Mahout is a Machine learning library that comes packaged with Apache Hadoop and forms an important part of the Hadoop ecosystem.

Mahout came into existence in 2008 as a subproject of Apache Lucene (an open source search engine). Lucene is an API that has an implementation of search, text mining, and information-retrieval techniques. Most of these search and text analytics internally apply Machine learning techniques. The recommendation engines that were built for the search engines started off under a new subproject called Mahout. Mahout means the rider of an elephant, signifying the running of Machine learning algorithms over Hadoop. It is a scalable Machine learning implementation that can run in a standalone mode (does not tightly integrate with Hadoop) as well.

Mahout is a set of some basic Machine learning Java libraries used for classification, clustering, pattern mining, and so on. Though Mahout today provides support for a subset of Machine learning algorithms...