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 1. Introduction to Machine learning

The goal of this chapter is to take you through the Machine learning landscape and lay out the basic concepts upfront for the chapters that follow. More importantly, the focus is to help you explore various learning strategies and take a deep dive into the different subfields of Machine learning. The techniques and algorithms under each subfield, and the overall architecture that forms the core for any Machine learning project implementation, are covered in depth.

There are many publications on Machine learning, and a lot of work has been done in past in this field. Further to the concepts of Machine learning, the focus will be primarily on specific practical implementation aspects through real-world examples. It is important that you already have a relatively high degree of knowledge in basic programming techniques and algorithmic paradigms; although for every programming section, the required primers are in place.

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

  • Introduction to Machine learning

  • A basic definition and the usage context

  • The differences and similarities between Machine learning and data mining, Artificial Intelligence (AI), statistics, and data science

  • The relationship with big data

  • The terminology and mechanics: model, accuracy, data, features, complexity, and evaluation measures

  • Machine learning subfields: supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Specific Machine learning techniques and algorithms are also covered under each of the machine learning subfields

  • Machine learning problem categories: Classification, Regression, Forecasting, and Optimization

  • Machine learning architecture, process lifecycle, and practical problems

  • Machine learning technologies, tools, and frameworks