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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

Regular expressions

Regular expressions, or regexes, are a powerful form of rule-based matching. Invented back in the 1950s, they were, for a very long time, the most useful way to find things in text and proponents argue that they still are.

No chapter on NLP would be complete without mentioning regexes. With that being said, this section is by no means a complete regex tutorial. It's intended to introduce the general idea and show how regexes can be used in Python, pandas, and spaCy.

A very simple regex pattern could be "a." This would only find instances of the lower-case letter a followed by a dot. However, regexes also allow you to add ranges of patterns; for example, "[a-z]." would find any lower-case letter followed by a dot, and "xy." would find only the letters "x" or "y" followed by a dot.

Regex patterns are case sensitive, so "A-Z" would only capture upper-case letters. This is useful if we are searching for expressions in which the spelling is frequently different; for example,...

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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.

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