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.
Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
Free Chapter
Applying Machine Learning to Structured Data
Utilizing Computer Vision
Understanding Time Series
Parsing Textual Data with Natural Language Processing
Using Generative Models
Reinforcement Learning for Financial Markets
Privacy, Debugging, and Launching Your Products
Fighting Bias
Bayesian Inference and Probabilistic Programming
Index

Preparing the data for the Keras library

In Chapter 1, Neural Networks and Gradient-Based Optimization, we saw that neural networks would only take numbers as inputs. The issue for us in our dataset is that not all of the information in our table is numbers, some of it is presented as characters.

Therefore, in this section, we're going to work on preparing the data for Keras so that we can meaningfully work with it.

Before we start, let's look at the three types of data, Nominal, Ordinal, and Numerical:

• Nominal data: This comes in discrete categories that cannot be ordered. In our case, the type of transfer is a nominal variable. There are four discrete types, but it does not make sense to put them in any order. For instance, TRANSFER cannot be more than CASH_OUT, so instead, they are just separate categories.

• Ordinal data: This also comes in discrete categories, but unlike nominal data, it can be ordered. For example, if coffee comes in large, medium, and small sizes, those are distinct...