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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

Markov chains - the car rental agency service

Suppose a car rental agency has three locations in Ottawa: A downtown location (labeled A), A East End location (labeled B), and a West End location (labeled C). The agency has a group of delivery drivers to serve all three locations. The agency's statistician has determined the following:

Of the calls to the Downtown location, 30% are delivered in the Downtown area, 30% are delivered in the East end, and 40% are delivered in the West end
Of the calls to the East end location, 40% are delivered in the downtown area, 40% are delivered in the East end, and 20% are delivered in the West end
Of the calls to the West end location, 50% are delivered in the Downtown area, 30% are delivered in the East end, and 20% are delivered in the West end

After making a delivery, a driver goes to the nearest location to make the next delivery. This way, the location of a specific driver is determined only by their previous location.

How to do it...

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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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