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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
8
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

We briefly discussed Mahout and Spark bindings. This is the future of Mahout, though a production-ready release is some time away. We learned the basic operations that can be performed on the various data structures and went through an example of applying these techniques to build a machine learning algorithm. I would encourage you to keep yourself updated on the development of Mahout and Spark bindings, and the best way would be to follow the Mahout Wiki.

In the next chapter, we will discuss end-to-end practical use cases of customer analytics. Most of the techniques used so far will be put into practice, and you will get an idea of a real-life analytics project.

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Tech Concepts
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Programming languages
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Learning Apache Mahout
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