Practical Machine Learning in R

Fred Nwanganga, Mike Chapple

• Publisher: Wiley
• Year: 2020
• ISBN:
  9781119591535
• Edition: 1
• Topic: Computer Science
• Subtopic: Software Development
• Index: Computer Science

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language

Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms.

Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more.

• Explores data management techniques, including data collection, exploration and dimensionality reduction
• Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering
• Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques
• Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost

Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.

PART I
Getting Started

1. Chapter 1: What Is Machine Learning?
2. Chapter 2: Introduction to R and RStudio
3. Chapter 3: Managing Data

Chapter 1
What Is Machine Learning?

Welcome to the world of machine learning! You're about to embark upon an exciting adventure discovering how data scientists use algorithms to uncover knowledge hidden within the troves of data that businesses, organizations, and individuals generate every day.

If you're like us, you often find yourself in situations where you are facing a mountain of data that you're certain contains important insights, but you just don't know how to extract that needle of knowledge from the proverbial haystack. That's where machine learning can help. This book is dedicated to providing you with the knowledge and skills you need to harness the power of machine learning algorithms. You'll learn about the different types of problems that are well-suited for machine learning solutions and the different categories of machine learning techniques that are most appropriate for tackling different types of problems.

Most importantly, we're going to approach this complex, technical field with a practical mind-set. In this book, our purpose is not to dwell on the intricate mathematical details of these algorithms. Instead, we'll focus on how you can put those algorithms to work for you immediately. We'll also introduce you to the R programming language, which we believe is particularly well-suited to approaching machine learning problems from a practical standpoint. But don't worry about programming or R for now. We'll get to that in Chapter 2. For now, let's dive in and get a better understanding of how machine learning works.

By the end of this chapter, you will have learned the following:

• How machine learning allows the discovery of knowledge in data
• How unsupervised learning, supervised learning, and reinforcement learning techniques differ from each other
• How classification and regression problems differ from each other
• How to measure the effectiveness of machine learning algorithms
• How cross-validation improves the accuracy of machine learning models

DISCOVERING KNOWLEDGE IN DATA

Our goal in the world of machine learning is to use algorithms to discover knowledge in our datasets that we can then apply to help us make informed decisions about the future. That's true regardless of the specific subject-matter expertise where we're working, as machine learning has applications across a wide variety of fields. For example, here are some cases where machine learning commonly adds value:

• Segmenting customers and determining the marketing messages that will appeal to different customer groups
• Discovering anomalies in system and application logs that may be indicative of a cybersecurity incident
• Forecasting product sales based on market and environmental conditions
• Recommending the next movie that a customer might want to watch based on their past activity and the preferences of similar customers
• Setting prices for hotel rooms far in advance based on forecasted demand

Of course, those are just a few examples. Machine learning can bring value to almost every field where discovering previously unknown knowledge is useful—and we challenge you to think of a field where knowledge doesn't offer an advantage!

Introducing Algorithms

As we proceed throughout this book, you'll see us continually referring to machine learning techniques as algorithms. This is a term from the world of computer science that comes up again and again in the world of data science, so ...