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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Using the AWS deep learning AMI

Amazon Web Services (AWS) provides an easy-to-use, preconfigured way to run deep learning in the cloud.

Visit for instructions on how to set up an Amazon Machine Image (AMI). While AMIs are paid, they can run longer than Kaggle kernels. So, for big projects, it might be worth using an AMI instead of a kernel.

To run the notebooks for this book on an AMI, first set up the AMI, then download the notebooks from GitHub, and then upload them to your AMI. You will have to download the data from Kaggle as well. See the Using data locally section for instructions.