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Apache Mahout Essentials

By : Jayani Withanawasam

3.7 (3)

Apache Mahout Essentials

3.7 (3)

By: Jayani Withanawasam

Overview of this book

If you are a Java developer or data scientist, haven't worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.

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. Introducing Apache Mahout

1. Introducing Apache Mahout

Machine learning in a nutshell

Machine learning applications

Machine learning libraries

The story so far

Apache Mahout

How Apache Mahout works?

From Hadoop MapReduce to Spark

When is it appropriate to use Apache Mahout?

Summary

2. Clustering

2. Clustering

Unsupervised learning and clustering

Applications of clustering

Types of clustering

K-Means clustering

Distance measure

K-Means clustering with MapReduce

Additional clustering algorithms

Text clustering

Optimizing clustering performance

Summary

3. Regression and Classification

3. Regression and Classification

Supervised learning

Predictive analytics' techniques

Classification versus regression

Linear regression with Apache Spark

Logistic regression with SGD

The Naïve Bayes algorithm

Hidden Markov Model

Summary

4. Recommendations

4. Recommendations

Collaborative versus content-based filtering

User-based recommenders

Item-based recommenders

Matrix factorization-based recommenders

Singular value decomposition

Summary

5. Apache Mahout in Production

5. Apache Mahout in Production

Introduction

Apache Mahout with Hadoop

Setting up Hadoop

Monitoring Hadoop

Setting up Mahout with Hadoop's fully-distributed mode

Troubleshooting Hadoop

Optimization tips

Summary

6. Visualization

6. Visualization

The significance of visualization in machine learning

D3.js

A visualization example for K-Means clustering

Summary

Index

Index

Classification versus regression

Methods for prediction can be divided into two general groups, based on the outcome of the prediction algorithm, as follows:

When the data is discrete, we will refer to it as classification. Discriminant analysis and pattern recognition are the other similar terms used for classification.
When the data is continuous, we will refer to it as regression. Other terms used for regression are smoothing and curve estimation.

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83

Tech Concepts

36

Programming languages

73

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