Welcome to Hands-On Deep Learning with R! This book will take you through all of the steps that are necessary to code deep learning models using the R statistical programming language. It begins with simple examples as the first step for those just getting started, along with a review of the foundational elements of deep learning for those with more experience. As you progress through this book, you will learn how to code increasingly complex deep learning solutions for a wide variety of tasks. However, regardless of the complexity, each chapter will carefully detail each step. This is so that all topics and concepts can be fully comprehended and the reason for every line of code is completely explained.
In this chapter, we will go through a quick overview of the machine learning process as it will form a base for the subsequent chapters of this book. We will look at processing a dataset to review techniques such as handling outliers and missing values. We will learn how to model data to brush up on the process of predicting an outcome and evaluating the results, and we will also review the most suitable metrics for various problems. We will look at improving a model using parameter tuning, feature engineering, and ensembling, and we will learn when to use different machine learning algorithms based on the task to solve.
This chapter will cover the following topics:
- An overview of machine learning
- Preparing data for modeling
- Training a model on prepared data
- Evaluating model results
- Improving model results
- Reviewing different algorithms
