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Machine Learning for Finance

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  • Practical machine learning for the finance sector
  • Build machine learning systems that support the goals of financial organizations
  • Think creatively about problems and how machine learning can solve them
  • Identify and reduce sources of bias from machine learning models
  • Apply machine learning to structured data, natural language, photographs, and written text related to finance
  • Use machine learning to detect fraud, forecast financial trends, analyze customer sentiments, and more
  • Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow

Machine learning skills are essential for anybody working in financial data analysis. Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks.

You will see how to use machine learning to automate manual tasks, identify and address systemic bias, and find new insights and patterns hidden in available data. Machine Learning for Finance encourages and equips you to find new ways to use data to serve an organization’s business goals.

Broad in scope yet deeply practical in approach, Machine Learning for Finance will help you to apply machine learning in all parts of a financial organization’s infrastructure. If you work or plan to work in fintech, and want to gain one of the most valuable skills in the sector today, this book is for you.

  • Build machine learning systems that will be useful across the financial services industry
  • Discover how machine learning can solve finance industry challenges
  • Gain the machine learning insights and skills fintech companies value most
Page Count 456
Course Length 13 hours 40 minutes
ISBN 9781789136364
Date Of Publication 29 May 2019
Catch – a quick guide to reinforcement learning
Markov processes and the bellman equation – A more formal introduction to RL
Advantage actor-critic models
Evolutionary strategies and genetic algorithms
Practical tips for RL engineering
Frontiers of RL


Jannes Klaas

Jannes Klaas is a quantitative researcher with a background in economics and finance. Currently a graduate student at Oxford University, he previously led two machine learning bootcamps and worked with several financial companies on data driven applications and trading strategies.

His active research interests include systemic risk as well as large-scale automated knowledge discovery.