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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Debugging data

You'll remember that back in the first chapter of this book, we discussed how machine learning models are a function of their training data, meaning that, for example, bad data will lead to bad models, or as we put it, garbage in, garbage out. If your project is failing, your data is the most likely culprit. Therefore, in this chapter we will start by looking at the data first, before moving on to look at the other possible issues that might cause our model to crash.

However, even if you have a working model, the real-world data coming in might not be up to the task. In this section, we will learn how to find out whether you have good data, what to do if you have not been given enough data, and how to test your data.

How to find out whether your data is up to the task

There are two aspects to consider when wanting to know whether your data is up to the task of training a good model:

  • Does the data predict what you want it to predict?

  • Do you have enough data?

To find out whether your...