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
Other Books You May Enjoy

Document similarity with word embeddings

The practical use case of word vectors is to compare the semantic similarity between documents. If you are a retail bank, insurance company, or any other company that sells to end users, you will have to deal with support requests. You'll often find that many customers have similar requests, so by finding out how similar texts are semantically, previous answers to similar requests can be reused, and your organization's overall service can be improved.

spaCy has a built-in function to measure the similarity between two sentences. It also comes with pretrained vectors from the Word2Vec model, which is similar to GloVe. This method works by averaging the embedding vectors of all the words in a text and then measuring the cosine of the angle between the average vectors. Two vectors pointing in roughly the same direction will have a high similarity score, whereas vectors pointing in different directions will have a low similarity score. This is visualized...