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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
5
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

Unsupervised learning

Unsupervised learning deals with unlabeled data. The objective is to observe structure in data and find patterns. Tasks like cluster analysis, association rule mining, outlier detection, dimensionality reduction, and so on can be modeled as unsupervised learning problems. As the tasks involved in unsupervised learning vary vastly, there is no single process outline that we can follow. We will follow the process of some of the most common unsupervised learning problems.

Cluster analysis

Cluster analysis is a subset of unsupervised learning that aims to create groups of similar items from a set of items. Real life examples could be clustering movies according to various attributes like genre, length, ratings, and so on. Cluster analysis helps us identify interesting groups of objects that we are interested in. It could be items we encounter in day-to-day life such as movies, songs according to taste, or interests of users in terms of their demography or purchasing patterns...

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Tech Concepts
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Programming languages
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Learning Apache Mahout
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