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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

Further reading

You made it to the end of the book! What are you going to do now? Read more books! Machine learning, and in particular, deep learning, is a fast-moving field, so any reading list risks being outdated by the time you read it. However, the following list aims to show you the most relevant books that have a safety net of remaining relevant over the coming years.

General data analysis

Wes McKinney, Python for Data Analysis, http://wesmckinney.com/pages/book.html.

Wes is the original creator of pandas, a popular Python data-handling tool that we saw in Chapter 2, Applying Machine Learning to Structured Data. pandas is a core component of any data science workflow in Python and will remain so for the foreseeable future. Investing in sound knowledge of the tools he presents is definitely worth your time.

Sound science in machine learning

Marcos Lopez de Prado, Advances in Financial Machine Learning, https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086.

Marcos...

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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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