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Machine Learning for Finance

You're reading from Machine Learning for Finance Principles and practice for financial insiders

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789136364

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Jannes Klaas

James Le

View More author details

Table of Contents (15) Chapters

Machine Learning for Finance

Contributors

Preface

Other Books You May Enjoy

1. Neural Networks and Gradient-Based Optimization

2. Applying Machine Learning to Structured Data FREE CHAPTER

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

Index

The feature engineering approach

The objective of feature engineering is to exploit the qualitative insight of humans in order to create better machine learning models. A human engineer usually uses three types of insight: intuition, expert domain knowledge, and statistical analysis. Quite often, it's possible to come up with features for a problem just from intuition.

As an example, in our fraud case, it seems intuitive that fraudsters will create new accounts for their fraudulent schemes and won't be using the same bank account that they pay for their groceries with.

Domain experts are able to use their extensive knowledge of a problem in order to come up with other such examples of intuition. They'll know more about how fraudsters behave and can craft features that indicate such behavior. All of these intuitions are then usually confirmed by statistical analysis, something that can even be used to open the possibilities of discovering new features.

Statistical analysis can sometimes turn...

You have been reading a chapter from

Machine Learning for Finance

Published in: May 2019

Publisher: Packt

ISBN-13: 9781789136364

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Authors (2)

James Le

James Le

James Le is currently a master's student studying Computer Science at Rochester Institute of Technology. He is pursuing a research path in Computer Vision using Deep Learning. Moreover, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.

See other products by James Le

Jannes Klaas

Jannes Klaas

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.

See other products by Jannes Klaas

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