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  • Book Overview & Buying Apache Mahout Essentials
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Apache Mahout Essentials

Apache Mahout Essentials

By : Jayani Withanawasam
3.7 (3)
Buy this Book
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Apache Mahout Essentials

Apache Mahout Essentials

3.7 (3)
By: Jayani Withanawasam
Buy this Book

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.
Table of Contents (8 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. Introducing Apache Mahout
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1. Introducing Apache Mahout
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Machine learning in a nutshell
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Machine learning applications
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Machine learning libraries
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The story so far
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Apache Mahout
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How Apache Mahout works?
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From Hadoop MapReduce to Spark
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When is it appropriate to use Apache Mahout?
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Summary
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2. Clustering
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2. Clustering
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Unsupervised learning and clustering
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Applications of clustering
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Types of clustering
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K-Means clustering
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Distance measure
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K-Means clustering with MapReduce
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Additional clustering algorithms
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Text clustering
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Optimizing clustering performance
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Summary
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3. Regression and Classification
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3. Regression and Classification
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Supervised learning
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Predictive analytics' techniques
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Classification versus regression
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Linear regression with Apache Spark
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Logistic regression with SGD
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The Naïve Bayes algorithm
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Hidden Markov Model
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Summary
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4. Recommendations
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4. Recommendations
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Collaborative versus content-based filtering
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User-based recommenders
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Item-based recommenders
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Matrix factorization-based recommenders
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Singular value decomposition
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Summary
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5. Apache Mahout in Production
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5. Apache Mahout in Production
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Introduction
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Apache Mahout with Hadoop
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Setting up Hadoop
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Monitoring Hadoop
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Setting up Mahout with Hadoop's fully-distributed mode
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Troubleshooting Hadoop
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Optimization tips
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Summary
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6. Visualization
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6. Visualization
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The significance of visualization in machine learning
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D3.js
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A visualization example for K-Means clustering
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Summary
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Index
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Index

Chapter 3. Regression and Classification

This chapter explains the regression and classification technique in machine learning and its implementation using different machine learning algorithms in Apache Mahout. The machine learning theory behind the algorithm and real-world applications with example scripts are also explained.

In this chapter, we will cover the following topics:

  • Supervised learning
  • Target variables and predictor variables
  • Predictive analytics techniques
  • Classification versus regression
  • Linear regression with Apache Spark
  • Logistic regression with Stochastic Gradient Descent (SGD)
  • Naïve Bayes algorithm
  • Hidden Markov Models (HMMs)
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Tech Concepts
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Programming languages
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Apache Mahout Essentials
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