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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Pages 570 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Atul Tripathi

Table of Contents (21) Chapters

Practical Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Introduction to Machine Learning

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

13. Case Study - Pricing Reinsurance Contracts

14. Case Study - Forecast of Electricity Consumption

Introduction

Hidden Markov models - EUR and USD

The EUR/USD is the most commonly traded pair in the Forex market. Their popularity can be attributed to the fact that each currency represents the world's two largest economic and trading blocks and many multinational corporations that conduct business across the Atlantic.

Price movements for this currency pair are generally related to factors that influence the value of either the EUR or the USD. As the world's most liquid currency pair, the EUR/USD offers tight spreads and constant liquidity for traders who are looking to instantly buy or sell. The combination of stability and volatility makes the EUR/USD an excellent pair for beginner and advanced traders. The EUR/USD pair offers traders high liquidity and has very tight and competitive spreads. The relative strength of the US economy and the European economy, which can be tracked through the daily news, generally influence this pair.

Getting ready

In order to apply the hidden Markov model to find different...

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Authors (1)

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

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