Machine Learning for Finance: Principles and practice for financial insiders
Jannes Klaas
Sold by Lucky's Textbooks, Dallas, TX, U.S.A.
AbeBooks Seller since 22 July 2022
New - Soft cover
Condition: New
Quantity: Over 20 available
Add to basketSold by Lucky's Textbooks, Dallas, TX, U.S.A.
AbeBooks Seller since 22 July 2022
Condition: New
Quantity: Over 20 available
Add to basketA 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.
Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies.
Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.
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