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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

Hierarchical clustering - Amazon rainforest burned between 1999-2010

Between 1999-2010, 33,000 square miles (85,500 square kilometers), or 2.8 percent of the Amazon rainforest burned down. This was found by NASA-led research. The main purpose of the research was to measure the extent of fire smolders under the forest canopy. The research found that burning forests destroys a much larger area compared to when forest lands are cleared for agriculture and cattle pasture. Yet, no correlation could be established between the fires and deforestation.

The answer to the query of no correlation between fires and deforestation lay in humidity data from the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA's Aqua satellite. The fire frequency coincides with low night-time humidity, which allowed the low-intensity surface fires to continue burning.

Getting ready

In order to perform hierarchical clustering, we shall be using a dataset collected on the Amazon rainforest, which burned from 1999-2010...

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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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