ABOUT THIS BOOK
* Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark
* Comprehensive practical solutions taking you into the future of machine learning
* Go a step further and integrate your machine learning projects with more » Hadoop
WHO THIS BOOK IS FOR
This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately.
WHAT YOU WILL LEARN
* Implement a wide range of algorithms and techniques for tackling complex data
* Get to grips with some of the most powerful languages in data science, including R, Python, and Julia
* Harness the capabilities of Spark and Mahout used in conjunction with Hadoop to manage and process data successfully
* Apply the appropriate Machine learning technique to address a real-world problem
* Get acquainted with deep learning and find out how neural networks are being used at the cutting edge of Machine learning
* Explore the future of Machine learning and dive deeper into polyglot persistence, semantic data, and more
This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While Machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.
We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learning's real-world application.
The book also explores cutting-edge advances in Machine learning, with worked examples and guidance on Deep learning and Reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced Machine learning methodologies. « less
Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout
***** About This Book *****
* Apply machine learning algorithms effectively in production environments with Apache Mahout
* Gain better insights into large, complex, and scalable datasets
* Fast-paced tutorial, covering the core concepts of Apache Mahout to implement machine learning on Big Data
***** more » Who This Book Is For *****
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.
***** What You Will Learn *****
* Get started with the fundamentals of Big Data, batch, and real-time data processing with an introduction to Mahout and its applications
* Understand the key machine learning concepts behind algorithms in Apache Mahout
* Apply machine learning algorithms provided by Apache Mahout in real-world practical scenarios
* Implement and evaluate widely-used clustering, classification, and recommendation algorithms using Apache Mahout
* Discover tips and tricks to improve the accuracy and performance of your results
* Set up Apache Mahout in a production environment with Apache Hadoop
* Glance at the Spark DSL advancements in Apache Mahout 1.0
* Provide dynamic and interactive data visualizations for Apache Mahout
* Build a recommendation engine for real-time use cases and use user-based and item-based recommendation algorithms
***** In Detail *****
Apache Mahout is a scalable machine learning library with algorithms for clustering, classification, and recommendations. It empowers users to analyze patterns in large, diverse, and complex datasets faster and more scalably.
This book is an all-inclusive guide to analyzing large and complex datasets using Apache Mahout. It explains complicated but very effective machine learning algorithms simply, in relation to real-world practical examples.
Starting from the fundamental concepts of machine learning and Apache Mahout, this book guides you through Apache Mahout's implementations of machine learning techniques including classification, clustering, and recommendations. During this exciting walkthrough, real-world applications, a diverse range of popular algorithms and their implementations, code examples, evaluation strategies, and best practices are given for each technique. Finally, you will learn vdata visualization techniques for Apache Mahout to bring your data to life. « less
Explore the Hadoop MapReduce v2 ecosystem to gain insights from very large datasets
Starting with installing Hadoop YARN, MapReduce, HDFS, and other Hadoop ecosystem components, with this book, you will soon learn about many exciting topics such as MapReduce patterns, using Hadoop to solve analytics, classifications, online marketing, recommendations, and data indexing and searching. more » You will learn how to take advantage of Hadoop ecosystem projects including Hive, HBase, Pig, Mahout, Nutch, and Giraph and be introduced to deploying in cloud environments.
Finally, you will be able to apply the knowledge you have gained to your own real-world scenarios to achieve the best-possible results. « less
In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big more » Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark.
Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naïve Bayes classifier and item and user-based recommendation. You will then work with clustering Mahout using the K-means algorithm and implement Mahout without MapReduce. Finish with a flourish by exploring end-to-end use cases on customer analytics and test analytics to get a real-life practical know-how of analytics projects.
Who This Book Is For
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. « less
Build and personalize your own classifiers using Apache Mahout
This book is a practical guide that explains the classification algorithms provided in Apache Mahout with the help of actual examples. Starting with the introduction of classification and model evaluation techniques, we will explore Apache Mahout and learn why it is a good choice for classification.
Next, more » you will learn about different classification algorithms and models such as the Naive Bayes algorithm, the Hidden Markov Model, and so on.
Finally, along with the examples that assist you in the creation of models, this book helps you to build a mail classification system that can be produced as soon as it is developed. After reading this book, you will be able to understand the concept of classification and the various algorithms along with the art of building your own classifiers. « less
Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook.
About the Technology
A computer system more » that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others.
About this Book
This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework.
This book is written for developers familiar with Java -- no prior experience with Mahout is assumed.
* Use group data to make individual recommendations
* Find logical clusters within your data
* Filter and refine with on-the-fly classification
* Free audio and video extras
*** Table of Contents ***
1. Meet Apache Mahout
2. PART 1 RECOMMENDATIONS
3. Introducing recommenders
4. Representing recommender data
5. Making recommendations
6. Taking recommenders to production
7. Distributing recommendation computations
8. PART 2 CLUSTERING
9. Introduction to clustering
10. Representing data
11. Clustering algorithms in Mahout
12. Evaluating and improving clustering quality
13. Taking clustering to production
14. Real-world applications of clustering
15. PART 3 CLASSIFICATION
16. Introduction to classification
17. Training a classifier
18. Evaluating and tuning a classifier
19. Deploying a classifier
20. Case study: Shop It To Me « less