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Learning Apache Mahout Classification

Learning Apache Mahout Classification by Ashish Gupta
English | Feb 26, 2015 | ISBN: 1783554959 | 130 Pages | PDF (Converted) | 4.48 MB

This book is a practical guide that explains the classification algorithms provided in Apache Mahout with the help of actual examples. Starting with the introduction of classification and model evaluation techniques, we will explore Apache Mahout and learn why it is a good choice for classification.
Build and personalize your own classifiers using Apache Mahout

About This Book

Explore the different types of classification algorithms available in Apache Mahout
Create and evaluate your own ready-to-use classification models using real world datasets
A practical guide to problems faced in classification with concepts explained in an easy-to-understand manner

Who This Book Is For

If you are a data scientist who has some experience with the Hadoop ecosystem and machine learning methods and want to try out classification on large datasets using Mahout, this book is ideal for you. Knowledge of Java is essential.

In Detail

Next, you will learn about different classification algorithms and models such as the Naive Bayes algorithm, the Hidden Markov Model, and so on.

Finally, along with the examples that assist you in the creation of models, this book helps you to build a mail classification system that can be produced as soon as it is developed. After reading this book, you will be able to understand the concept of classification and the various algorithms along with the art of building your own classifiers.
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Tags: Learning, Apache, Mahout, Classification

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