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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Now that we have finished the first chapter in this exciting journey, I've got a challenge for you! You'll find some exercises that you can do that are all themed around what we've covered in this chapter!

So, why not try to do the following:

  1. Expand the two-layer neural network in Python to three layers.

  2. Within the GitHub repository, you will find an Excel file called 1 Excel Exercise. The goal is to classify three types of wine by their cultivar data. Build a logistic regressor to this end in Excel.

  3. Build a two-layer neural network in Excel.

  4. Play around with the hidden layer size and learning rate of the 2-layer neural network. Which options offer the lowest loss? Does the lowest loss also capture the true relationship?