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Table Of Contents

Learning Apache Mahout

By : Chandramani Tiwary

3 (1)

Learning Apache Mahout

3 (1)

By: Chandramani Tiwary

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Introduction to Mahout

1. Introduction to Mahout

Why Mahout

When Mahout

How Mahout

Summary

2. Core Concepts in Machine Learning

2. Core Concepts in Machine Learning

Supervised learning

Unsupervised learning

Recommender system

Model efficacy

Summary

3. Feature Engineering

3. Feature Engineering

Feature engineering

Summary

4. Classification with Mahout

4. Classification with Mahout

Classification

Logistic regression

Adaptive regression model

Code example with logistic regression

Random forest

Naïve Bayes classifier

Summary

5. Frequent Pattern Mining and Topic Modeling

5. Frequent Pattern Mining and Topic Modeling

Frequent pattern mining

Importing the Mahout source code into Eclipse

Frequent pattern mining with Mahout

Summary

6. Recommendation with Mahout

6. Recommendation with Mahout

Collaborative filtering

Summary

7. Clustering with Mahout

7. Clustering with Mahout

k-means

Canopy clustering

Fuzzy k-means

A Mahout command-line example

A Mahout Java example

Summary

8. New Paradigm in Mahout

8. New Paradigm in Mahout

Moving beyond MapReduce

Apache Spark

In-core types

Spark Mahout basics

Linear regression with Mahout Spark

Summary

9. Case Study – Churn Analytics and Customer Segmentation

9. Case Study – Churn Analytics and Customer Segmentation

Churn analytics

Summary

10. Case Study – Text Analytics

10. Case Study – Text Analytics

Text analytics

Clustering text

Categorizing text

Summary

Index

Index

Chapter 3. Feature Engineering

This chapter discusses arguably the most important step in solving a machine learning problem. Feature engineering involves the preparation and representation of data on which the models can be trained. A good feature set is compulsory for the success of a modeling project. In this chapter, we are going to cover the following topics:

Feature construction
Feature extraction
Feature selection
Dimensionality reduction

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

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Learning Apache Mahout

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