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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

Chapter 2. Applying Machine Learning to Structured Data

Structured data is a term used for any data that resides in a fixed field within a record or file, two such examples being relational databases and spreadsheets. Usually, structured data is presented in a table in which each column presents a type of value, and each row represents a new entry. Its structured format means that this type of data lends itself to classical statistical analysis, which is also why most data science and analysis work is done on structured data.

In day-to-day life, structured data is also the most common type of data available to businesses, and most machine learning problems that need to be solved in finance deal with structured data in one way or another. The fundamentals of any modern company's day-to-day running is built around structured data, including, transactions, order books, option prices, and suppliers, which are all examples of information usually collected in spreadsheets or databases.

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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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