Machine Learning for Finance: Principles and practice for financial insiders

by Jannes Klaas

A guide to advances in machine learning for financial professionals, with working Python code Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including in insurance, transactions, and lending. It 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 the advanced ML concepts and ideas that can be applied in a wide variety of ways. The book shows how machine learning works on structured data, text, images, and time series. It includes coverage of generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. It discusses how to fight bias in machine learning and ends with an exploration of Bayesian inference and probabilistic programming. This book is ideal for readers who understand math and Python, and want to adopt machine learning in financial applications. The book assumes college-level knowledge of math and statistics.

456 pages, Paperback

Published May 30, 2019

Reviews

[Sercan]

If you are looking for a book about machine learning for finance, keep looking. If you are looking for a hands-on introduction to machine learning (in particular deep learning) with lots of code examples and you are not bothered by lack of mathematical depth, this book might do.

[Ifor Williams]

Superb

[Luiz]

The title is heavily misleading. This book finance content is 1% (almost empty)