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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
4
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

Feature engineering

Let's start by understanding what is meant by feature engineering. Feature engineering is performed after data cleansing and preparation, before or even during model training. It aims to provide better representation of the data to the machine learning algorithm. Feature engineering as a process has multiple outcomes and can impact the overall modeling exercise in many ways. Feature engineering can be focused to increase model accuracy and generalization, decrease the computation requirements for large and wide datasets, and make the model simpler. Generally, a practitioner aims to do all of these. Feature engineering can be divided into four major tasks: feature construction, feature extraction, feature selection, and dimensionality reduction. We will discuss the four tasks shortly.

Before we discuss feature engineering, let's revisit the definition of features first:

  • Raw data comes with many attributes. For example, for structured data such as a database table...
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Tech Concepts
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Programming languages
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Learning Apache Mahout
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