Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
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In this chapter, you learned about the main algorithms in RL, Q-learning, policy gradients, and evolutionary strategies. You saw how these algorithms could be applied to trading and learned about some of the pitfalls of applying RL. You also saw the direction of current research and how you can benefit from this research today. At this point in the book, you are now equipped with a number of advanced machine learning algorithms, which are hopefully useful to you when developing machine learning models.

In the next chapter, we will discuss the practicalities of developing, debugging, and deploying machine learning systems. We will break out of the data-science sandbox and get our models into the real world.