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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

Local regression - understanding drought warnings and impact

Drought is a natural hazard which is measured and characterized by lower-than-expected or lower-than-normal rainfall. This condition, when prolonged over a longer-than-normal time period is insufficient to meet the demands of human activities and hazardous to the environment. Drought is a temporary phenomenon. Three main distinguishing features of droughts are intensity, duration, and spatial coverage. A drought early warning system can help to identify the climatic changes, understand water supply trends, and prepare for upcoming emergencies. The drought warning can help decision-makers take appropriate measures to face the upcoming challenge. They can then measure the severity of the impact and understand the underlying causes of vulnerability to reduce risk for a particular location and for a specific group of people or economic sector.

Getting ready

Let's get started with the recipe.

Step 1 - collecting and describing data

The...

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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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