In the last chapter, we learned how to design trading strategies, create trading signals, and implement advanced concepts, such as seasonality in trading instruments. Understanding those concepts in greater detail is a vast field comprising stochastic processes, random walks, martingales, and time series analysis, which we leave to you to explore at your own pace.

So what's next? Let's look at an even more advanced method of prediction and forecasting:
statistical inference and prediction. This is known as machine learning, the fundamentals of
which were developed in the 1800s and early 1900s and have been worked on ever since. Recently, there has been a resurgence in interest in machine learning algorithms and applications owing to the availability of extremely cost-effective processing power and the easy availability of large datasets. Understanding machine learning techniques in great detail is a massive field at the intersection of linear algebra, multivariate calculus, probability theory, frequentist and Bayesian statistics, and an in-depth analysis of machine learning is beyond the scope of a single book. Machine learning methods, however, are surprisingly easily accessible in Python and quite intuitive to understand, so we will explain the intuition behind the methods and see how they find applications in algorithmic trading. But first, let's introduce some basic concepts and notation that we will need for the rest of this chapter.

This chapter will cover the following topics:

• Understanding the terminology and notations
• Creating predictive models that predict price movement using linear regression methods
• Creating predictive models that predict buy and sell signals using linear classification methods