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Hands-On Automated Machine Learning

You're reading from Hands-On Automated Machine Learning A beginner's guide to building automated machine learning systems using AutoML and Python

Product type Paperback

Published in Apr 2018

Publisher Packt

ISBN-13 9781788629898

Length 282 pages

Edition 1st Edition

Languages

Python

Tools

Assemble

Concepts

Machine Learning

Authors (2):

Das

Mert Cakmak

View More author details

Table of Contents (15) Chapters

Title Page

Copyright and Credits

Packt Upsell

Contributors

Preface

1. Introduction to AutoML FREE CHAPTER

2. Introduction to Machine Learning Using Python

3. Data Preprocessing

4. Automated Algorithm Selection

5. Hyperparameter Optimization

6. Creating AutoML Pipelines

7. Dive into Deep Learning

8. Critical Aspects of ML and Data Science Projects

1. Other Books You May Enjoy

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Index

Linear regression

Let's begin our triple W session with linear regression first.

What is linear regression?

It is the traditional and most-used regression analysis. It is studied rigorously and used widely for practical purposes. Linear regression is a method for determining the relationship between a dependent variable (y) and one or more independent variables (x). This derived relationship can be used to predict an unexplained y from observed x's. Mathematically, if x is an independent variable (commonly known as the predictor) and y is a dependent variable (also known as the target), the relationship is expressed as follows:

Where m is the slope of line, b is the intercept of the best-fit regression line, and ε is the error term that is a deviation of the actual and predicted values.

This is the equation for simple linear regression, as it involves only one predictor (x) and one target (y). When there are multiple predictors involved to predict a target, it is known as multiple linear regression...

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Authors (2)

Das

Das

Sibanjan Das is a Business Analytics and Data Science consultant. He has extensive experience in implementing predictive analytics solutions in Business Systems and IoT. An enthusiastic and passionate professional about technology and innovation, he has the passion for wrangling with data since early days of his career. Sibanjan holds a Masters IT degree with major in Business Analytics from Singapore Management University and holds several industry certifications such as OCA, OCP and CSCMS.

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Mert Cakmak

Mert Cakmak

Umit Mert Cakmak is a data scientist at IBM, where he excels at helping clients solve complex data science problems, from inception to delivery of deployable assets. His research spans multiple disciplines beyond his industry and he likes sharing his insights at conferences, universities, and meet-ups.

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