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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

An introductory guide to spaCy

spaCy is a library for advanced NLP. The library, which is pretty fast to run, also comes with a range of useful tools and pretrained models that make NLP easier and more reliable. If you've installed Kaggle, you won't need to download spaCy, as it comes preinstalled with all the models.

To use spaCy locally, you will need to install the library and download its pretrained models separately.

To install the library, we simply need to run the following command:

$ pip install -U spacy
$ python -m spacy download en

Note

Note: This chapter makes use of the English language models, but more are available. Most features are available in English, German, Spanish, Portuguese, French, Italian, and Dutch. Entity recognition is available for many more languages through the multi-language model.

The core of spaCy is made up of the Doc and Vocab classes. A Doc instance contains one document, including its text, tokenized version, and recognized entities. The Vocab class, meanwhile...

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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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