This chapter (along with previous two) acts as the backbone for the entire book. It provides a tour of the machine learning paradigm—the features and functionalities available through the IBM Cloud and IBM Watson platforms, with a focus on well-known approaches and algorithms. We'll start the chapter by giving a somewhat practical background to what model evaluation, model selection, and algorithm selection in machine learning entail. Next, we will look at how the IBM Cloud platform can help to simplify and fast-track the entire process.

Moreover, this chapter will discuss machine learning algorithms for classification and regression problems, and again approach these topics using the IBM Cloud platform. By the end of the chapter, the reader should be able to not only understand the concepts involved in selecting an appropriate classification technique and estimators, but be able to build and deploy basic machine learning models for the data at hand, using IBM Cloud.

We'll divide this chapter into the following areas:

• Model selection
• Testing the model
• Classification
• Regression
• Testing the predictive capability