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Statistics for Machine Learning

You're reading from Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781788295758

Length 442 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Pratap Dangeti

View More author details

Table of Contents (10) Chapters

Preface

1. Journey from Statistics to Machine Learning FREE CHAPTER

2. Parallelism of Statistics and Machine Learning

3. Logistic Regression Versus Random Forest

4. Tree-Based Machine Learning Models

5. K-Nearest Neighbors and Naive Bayes

6. Support Vector Machines and Neural Networks

7. Recommendation Engines

8. Unsupervised Learning

9. Reinforcement Learning

Support vector machines working principles

Support vector machines are mainly classified into three types based on their working principles:

Maximum margin classifiers
Support vector classifiers
Support vector machines

Maximum margin classifier

People usually generalize support vector machines with maximum margin classifiers. However, there is much more to present in SVMs compared to maximum margin classifiers, which we will be covering in this chapter. It is feasible to draw infinite hyperplanes to classify the same set of data upon, but the million dollar question, is which one to consider as an ideal hyperplane? The maximum margin classifier provides an answer to that: the hyperplane with the maximum margin of separation width.

Hyperplanes: Before going forward, let us quickly review what a hyperplane is. In n-dimensional space, a hyperplane is a flat affine subspace of dimension n-1. This means, in 2-dimensional space, the hyperplane is a straight line which separates the 2-dimensional space...

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Authors (1)

Pratap Dangeti

Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

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