What this book covers

Chapter 1, Introducing Machine Learning Predictive Models, introduces you to the theory behind predictive models, looking at how they work and providing an insight into types of predictive modeling, such as the neural network model, which is explained in brief in this chapter.

Chapter 2, Getting Started with Machine Learning, introduces you to the implementation of a neural network model, and gives an insight into the implementation of Support Vector Machines (SVMs) as well.

Chapter 3, Understanding Models, explains different types of models and the situations in which each of them should ideally be used.

Chapter 4, Improving Individual Models, shows you different ways in which we can improve our models. This chapter will show you four methods to improve the accuracy of your model.

Chapter 5, Advanced Ways of Improving Models, focuses on combining different models in different ways to get increasingly better results. In this chapter, we will see how a certain part of a dataset, which doesn't contribute much to the results of a neural network model, performs very well on the CHAID and C5.0 decision tree models. We will also see how to model the errors to prepare our models.