Book Image

Practical Machine Learning

By : Sunila Gollapudi
Book Image

Practical Machine Learning

By: Sunila Gollapudi

Overview of this book

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and 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. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements 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.
Table of Contents (23 chapters)
Practical Machine Learning
About the Author
About the Reviewers


In this chapter we have explored the qualifiers of large datasets, their common characteristics, the problems of repetition, and the reasons for the hyper-growth in volumes; in fact, the big data context.

The need for applying conventional Machine learning algorithms to large datasets has given rise to new challenges for Machine learning practitioners. Traditional Machine learning libraries do not quite support, processing huge datasets. Parallelization using modern parallel computing frameworks, such as MapReduce, have gained popularity and adoption; this has resulted in the birth of new libraries that are built over these frameworks.

The concentration was on methods that are suitable for massive data, and have potential for the parallel implementation. The landscape of Machine learning applications has changed dramatically in the last decade. Throwing more machines doesn't always prove to be a solution. There is a need to revisit traditional algorithms and models in the way they...