In this book, we will learn about the implementation of many of the common machine learning algorithms you interact with in your daily life. There will be plenty of math, theory, and tangible code examples to satisfy even the biggest machine learning junkie and, hopefully, you'll pick up some useful Python tricks and practices along the way. We are going to start off with a very brief introduction to supervised learning, sharing a real-life machine learning demo; getting our Anaconda environment setup done; learning how to measure the slope of a curve, Nd-curve, and multiple functions; and finally, we'll discuss how we know whether or not a model is good. In this chapter, we will cover the following topics:
- An example of supervised learning in action
- Setting up the environment
- Supervised learning
- Hill climbing and loss functions
- Model evaluation and data splitting
