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

Instance-based learning (IBL)

The IBL technique approaches learning by simply storing the provided training data and using it as a reference for predicting/determining the behavior of a new query. As learned in Chapter 1, Introduction to Machine learning, instances are nothing but subsets of datasets. The instance-based learning model works on an identified instance or groups of instances that are critical to the problem. The results across instances are compared and can include an instance of new data as well. This comparison uses a particular similarity measure to find the best match and predict. Since it uses historical data stored in memory, this learning technique is also called memory-based or case-based learning. Here, the focus is on the representation of the instances and similarity measures for comparison between them.

Every time a new query instance is received for processing, a set of similar, related instances are retrieved from memory, and then this data is used to classify...