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Machine Learning for Developers

You're reading from   Machine Learning for Developers Uplift your regular applications with the power of statistics, analytics, and machine learning

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781786469878
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Md Mahmudul Hasan Md Mahmudul Hasan
Author Profile Icon Md Mahmudul Hasan
Md Mahmudul Hasan
Rodolfo Bonnin Rodolfo Bonnin
Author Profile Icon Rodolfo Bonnin
Rodolfo Bonnin
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction - Machine Learning and Statistical Science 2. The Learning Process FREE CHAPTER 3. Clustering 4. Linear and Logistic Regression 5. Neural Networks 6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Recent Models and Developments 9. Software Installation and Configuration

Regression analysis

This chapter will begin with an explanation of the general principles. So, let's ask the fundamental question: what's regression?

Before all considerations, regression is basically a statistical process. As we saw in the introductory section, regression will involve a set of data that has some particular probability distribution. In summary, we have a population of data that we need to characterize.

And what elements are we looking for in particular, in the case of regression? We want to determine the relationship between an independent variable and a dependent variable that optimally adjusts to the provided data. When we find such a function between the described variables, it will be called the regression function.

There are a large number of function types available to help us model our current data, the most common example being the linear, polynomial...

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