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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Chapter 2. Applying Machine Learning to Structured Data

Structured data is a term used for any data that resides in a fixed field within a record or file, two such examples being relational databases and spreadsheets. Usually, structured data is presented in a table in which each column presents a type of value, and each row represents a new entry. Its structured format means that this type of data lends itself to classical statistical analysis, which is also why most data science and analysis work is done on structured data.

In day-to-day life, structured data is also the most common type of data available to businesses, and most machine learning problems that need to be solved in finance deal with structured data in one way or another. The fundamentals of any modern company's day-to-day running is built around structured data, including, transactions, order books, option prices, and suppliers, which are all examples of information usually collected in spreadsheets or databases.

This chapter...