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 6. Instance and Kernel Methods Based Learning

We have covered Decision tree models for solving classification and regression problems in the previous chapter. In this chapter, we will cover two important models of supervised and unsupervised learning techniques which are the Nearest Neighbors method, which uses the instance-based learning model, and the Support Vector Machines (SVM) model, which uses kernel methods based learning model. For both methods, we will learn the basics of the technique and see how it can be implemented in Apache Mahout, R, Julia, Apache Spark, and Python. The following figure depicts different learning models covered in this book and the techniques highlighted will be dealt in covered in this chapter.

The following topics are covered in-depth in this chapter:

  • Instance-based learning models

  • Introduction to instance-based learning

  • Lazy and eager learning

  • A brief look at different algorithms/approaches of instance-based learning techniques Nearest Neighbor method...