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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Length 570 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Atul Tripathi

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Machine Learning FREE CHAPTER

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

Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Poisson regression - understanding species present in Galapagos Islands

The Galapagos Islands are situated in the Pacific Ocean about 1000 km from the Ecuadorian coast. The archipelago consists of 13 islands, five of which are inhabited. The islands are rich in flora and fauna. Scientists are still perplexed by the fact that such a diverse set of species can flourish in such a small and remote group of islands.

Getting ready

In order to complete this recipe we shall be using species dataset. The first step is collecting the data.

Step 1 - collecting and describing the data

We will utilize the number of species dataset titled gala that is available at https://github.com/burakbayramli/kod/blob/master/books/Practical_Regression_Anove_Using_R_Faraway/gala.txt .

The dataset includes 30 cases and seven variables in the dataset. The seven numeric measurements include the following:

Species
Endemics
Area
Elevation
Nearest
Scruz
Adjcacent

How to do it...

Let's get into the details.

Step 2 - exploring the data...

The rest of the chapter is locked

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Authors (1)

Atul Tripathi

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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