Machine Learning for Finance: The Practical Guide to Using Data-Driven Algorithms in Banking, Insurance, and Investments

✍ Scribed by Jannes Klaas

• Publisher: Packt Publishing
• Year: 2019
• Tongue: English
• Leaves: 456
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

About the author
About the reviewer
Table of Contents
Preface
1 Neural Networks and Gradient-Based Optimization
2 Applying Machine Learning to Structured Data
3 Utilizing Computer Vision
4 Understanding Time Series
5 Parsing Textual Data with Natural Language Processing
6 Using Generative Models
7 Reinforcement Learning for Financial Markets
8 Privacy, Debugging, and Launching Your Products
9 Fighting Bias
10 Bayesian Inference and Probabilistic Programming
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Index