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

Reinforcement learning solution methods

In this section, we will discuss in detail some of the methods to solve Reinforcement Learning problems. Specifically, dynamic programming (DP), Monte Carlo method, and temporal-difference (TD) learning. These methods address the problem of delayed rewards as well.

Dynamic Programming (DP)

DP is a set of algorithms that are used to compute optimal policies given a model of environment like Markov Decision Process. Dynamic programming models are both computationally expensive and assume perfect models; hence, they have low adoption or utility. Conceptually, DP is a basis for many algorithms or methods used in the following sections:

  1. Evaluating the policy: A policy can be assessed by computing the value function of the policy in an iterative manner. Computing value function for a policy helps find better policies.

  2. Improving the policy: Policy improvement is a process of computing the revised policy using its value function information.

  3. Value iteration...