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

Chapter 2. Machine learning and Large-scale datasets

We have seen a dramatic change in the way data has been handled in the recent past with the advent of big data. The field of Machine learning has seen the need to include scaling up strategies to handle the new age data requirements. This actually means that some of the traditional Machine learning implementations will not all be relevant in the context of big data now. Infrastructure and tuning requirements are now the challenges with the need to store and process large scale data complimented by the data format complexities.

With the evolution of hardware architectures, accessibility of cheaper hardware with distributed architectures and new programming paradigms for simplified parallel processing options, which can now be applied to many learning algorithms, we see a rising interest in scaling up the Machine learning systems.

The topics listed next are covered in-depth in this chapter:

  • An introduction to big data and typical challenges...