Learning Apache Mahout is aimed at providing a strong foundation in machine learning using Mahout. This book is ideal for learning the core concepts of machine learning and the basics of Mahout. This book will go from the basics of Mahout and machine learning, to feature engineering and the implementation of various machine learning algorithms in Mahout. Algorithm usage examples will be explained using both the Mahout command line and its Java API. We will conclude the book with two chapters of end-to-end case studies. Ideally, chapters 1, 2 and 3 should be read sequentially, chapters 4 to 8 in any order, and chapters 9 and 10 after chapter 1 to 8 have been completed.
Chapter 1, Introduction to Mahout, covers the setup of the learning environment, installation, and the configuration of the various tools required for this book. It will discuss the need for a machine learning library such as Mahout and introduce the basics of Mahout with command line and code examples.
Chapter 2, Core Concepts in Machine Learning, covers the fundamental concepts in machine learning. It will discuss the important steps involved in a machine learning project, such as data processing, model training, and efficacy, and provides an intuitive explanation of different algorithms.
Chapter 3, Feature Engineering, covers the most important phase of a machine learning project, feature extraction and representation. It will discuss common data preprocessing tasks, manual and automated feature transformation, feature selection, and dimensionality reduction.
Chapter 4, Classification with Mahout, covers classification algorithms implemented in Mahout. It will discuss the important phases of building a classifier, such as preprocessing data, creating a train and test set, and measuring model efficacy. The algorithms that will be covered are logistic regression, random forest, and naïve Bayes.
Chapter 5, Frequent Pattern Mining and Topic Modeling, covers algorithms for frequent pattern mining and topic modeling. This chapter will provide an intuitive explanation of the algorithms and include both command line and code examples, while also providing practical examples.
Chapter 6, Recommendation with Mahout, covers algorithms to build recommender systems in Mahout. It will discuss item-based and user-based recommenders. This chapter will provide an intuitive explanation of the algorithms and include both command line and code examples, while also providing practical examples.
Chapter 7, Clustering with Mahout, covers algorithms to perform clustering in Mahout. It will discuss algorithms such as k-means, fuzzy k-means, streaming k-means, and so on. This chapter will provide an intuitive explanation of the algorithm and include both command line and code examples, while also providing practical examples.
Chapter 8, New Paradigm in Mahout, covers the porting of Mahout on top of Apache Spark. It will discuss the installation and configuration of Mahout and Spark, explain the important concepts of Spark and Mahout binding, and cover some basic examples.
Chapter 9, Case Study – Churn Analytics and Customer Segmentation, covers the steps involved in a machine learning project from start to finish. It will discuss all the important steps that need to be performed for a successful machine learning project. It will take a couple of use cases from customer analytics, churn analytics, and customer segmentation, to walk through the process.
Chapter 10, Case Study – Text Analytics, covers the steps involved in a text analytics project. It will discuss the vector space model of representing text, text clustering, and classification.
For this book, you will need the following software:
Java 1.6 or higher
Maven 2.0 or higher
Hadoop 1.2.1
Eclipse with Maven plug-in
Mahout 0.9
Python
R
We will cover every software needed for this book in the corresponding chapters. All the examples in the book have been coded using the Ubuntu 12.04 LTS release.
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 ideal for you. This book is good for self-learners who want to learn the fundamental concepts of machine learning and the practical implementations of Mahout. Some familiarity with shell scripts, Python, and R is assumed, but no prior experience is required.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The output of the sed command is saved to the new file adult.data.csv."
Any command-line input or output is written as follows:
sudo pip install pandas
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Once the search results are displayed hit Install and follow the instructions."
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