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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

Product type Paperback

Published in Oct 2017

Publisher Packt

ISBN-13 9781786469878

Length 270 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Bonnin

Hasan

View More author details

Table of Contents (17) Chapters

Title Page

Credits

Foreword

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. Introduction - Machine Learning and Statistical Science FREE CHAPTER

2. The Learning Process

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, and exponential.

These techniques will aim to determine an objective function, which in our case will output a finite...

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Authors (2)

Bonnin

Bonnin

Rodolfo Bonnin is a systems engineer and Ph.D. student at Universidad Tecnolgica Nacional, Argentina. He has also pursued parallel programming and image understanding postgraduate courses at Universitt Stuttgart, Germany. He has been doing research on high-performance computing since 2005 and began studying and implementing convolutional neural networks in 2008, writing a CPU- and GPU-supporting neural network feedforward stage. More recently he's been working in the field of fraud pattern detection with Neural Networks and is currently working on signal classification using machine learning techniques. He is also the author of Building Machine Learning Projects with Tensorflow and Machine Learning for Developers by Packt Publishing.

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Hasan

Hasan

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