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  • Book Overview & Buying Learning Apache Mahout
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Learning Apache Mahout

Learning Apache Mahout

By : Chandramani Tiwary
3 (1)
Buy this Book
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Learning Apache Mahout

Learning Apache Mahout

3 (1)
By: Chandramani Tiwary
Buy this Book

Overview of this book

If you are a Java developer and want to use Mahout and machine learning to solve Big Data Analytics use cases then this book is for you. Familiarity with shell scripts is assumed but no prior experience is required.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introduction to Mahout
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1. Introduction to Mahout
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Why Mahout
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When Mahout
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How Mahout
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Summary
2
2. Core Concepts in Machine Learning
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2. Core Concepts in Machine Learning
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Supervised learning
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Unsupervised learning
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Recommender system
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Model efficacy
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Summary
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3. Feature Engineering
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3. Feature Engineering
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Feature engineering
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Summary
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4. Classification with Mahout
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4. Classification with Mahout
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Classification
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Logistic regression
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Adaptive regression model
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Code example with logistic regression
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Random forest
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Naïve Bayes classifier
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Summary
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5. Frequent Pattern Mining and Topic Modeling
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5. Frequent Pattern Mining and Topic Modeling
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Frequent pattern mining
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Importing the Mahout source code into Eclipse
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Frequent pattern mining with Mahout
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Summary
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6. Recommendation with Mahout
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6. Recommendation with Mahout
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Collaborative filtering
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Summary
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7. Clustering with Mahout
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7. Clustering with Mahout
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k-means
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Canopy clustering
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Fuzzy k-means
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A Mahout command-line example
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A Mahout Java example
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Summary
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8. New Paradigm in Mahout
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8. New Paradigm in Mahout
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Moving beyond MapReduce
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Apache Spark
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In-core types
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Spark Mahout basics
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Linear regression with Mahout Spark
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Summary
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9. Case Study – Churn Analytics and Customer Segmentation
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9. Case Study – Churn Analytics and Customer Segmentation
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Churn analytics
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Summary
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10. Case Study – Text Analytics
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10. Case Study – Text Analytics
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Text analytics
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Clustering text
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Categorizing text
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Summary
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Index
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Index

Summary

In this chapter, we discussed the guiding principle of Mahout and tried out some examples to get a hands-on feel of Mahout. We discussed why, when, and how to use Mahout and walked through the installation steps of the required tools and software. We then learned how to use Mahout from the command line and from the code, and finally concluded with a comparison between the parallel and the single-machine execution of Mahout.

This is the beginning of what will hopefully be an exciting journey. In the forthcoming chapters, we will discuss a lot of practical applications for Mahout. In the next chapter, we will discuss the core concepts of machine learning. A clear understanding of the concepts of different machine learning algorithms is of paramount importance for a successful career in data analytics.

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Learning Apache Mahout
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