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Practical Machine Learning Cookbook

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

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785280511
Length 570 pages
Edition 1st Edition
Languages
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Author (1):
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Atul Tripathi Atul Tripathi
Author Profile Icon Atul Tripathi
Atul Tripathi
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Table of Contents (15) Chapters Close

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 13. Case Study - Pricing Reinsurance Contracts 14. Case Study - Forecast of Electricity Consumption

An overview of classification

Linear regression models present response variables that are quantitative in nature. However, certain responses are qualitative in nature. Responses such as attitudes (strongly disagree, disagree, neutral, agree, and strongly agree) are qualitative in nature. Predicting a qualitative response for an observation can be referred to as classifying that observation, since it involves assigning the observation to a category or class. Classifiers are an invaluable tool for many tasks today, such as medical or genomics predictions, spam detection, face recognition, and finance.

An overview of classification

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Practical Machine Learning Cookbook
Published in: Apr 2017
Publisher: Packt
ISBN-13: 9781785280511
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