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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

Binary clustering - math test

In the education system tests and examinations are major features. The advantage of examination system is that it can be one of the ways to differentiate between good and poor performers. The examination system puts the onus on students to upgrade for next standard for which they should appear and pass exams. It creates responsibility on students to study on regular basis. The exam systems prepare the students to meet the challenges of future. It helps them to analyze reason and communicate their ideas effectively in a fixed time period. On the other hand few draw backs are noticed such as slow learners cannot perform well in test and this creates inferior complexity among students.

Getting ready

In order to perform binary clustering, we shall be using a dataset collected on math tests.

Step 1 - collecting and describing data

The dataset titled math test shall be used. This is available in the TXT format titled math test.txt. The dataset is in standard format. There...

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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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