Python: This Book Includes: The Guide for Beginners, Machine Learning

Josh Hugh Learning

ISBN 10: 1673217095 / ISBN 13: 9781673217094

Published by Independently Published, 2019

About this Item

This item is printed on demand. New copy - Usually dispatched within 5-9 working days 426.

Seller Inventory # C9781673217094

Bibliographic Details

• Title: Python: This Book Includes: The Guide for Beginners, Machine Learning
• Publisher: Independently Published
• Publication Date: 2019
• Language: English
• ISBN 10: 1673217095
• ISBN 13: 9781673217094
• Binding: Paperback / softback
• Condition: New

About this title

Synopsis

If you are a Beginner Who Wants to Know the Basics of Python Programming and the Best Machine Learning Algorithms › › › just keep reading › › ›

Would you like to program apps, games, softwares and much more but don't know how ?

Do you see all those Machine Learning Algorithms but getting nowhere? Don't give up hope!

This bundle gives you the oportunity to learn in a simple way the tools to handle your own data with the right algorithm.

You will discover both Python programming and Machine Learning which, combined, will give you the ability to create some of the best data analysis that you need.

Even if you have never used this programs before, with this guidebooks you will be able to do that in no time at all.

2 books in 1

In Python for Beginners You Will Learn:

• The Python language

• Data and variables

• Control flow tools

• The files

• A look at the classes

• Creating your own modules

• The regular expressions

• Networking

• The process of multithreading

• The way to access databases with Python

• How GUI programming works

In Python Machine Learning You Will Learn:

• The basics of machine learning and why it is so important to learn.

• The importance of data and how to use these in machine learning.

• Some of the supervised learning algorithms that work with regressions.

• How to work with regularization and avoid the issue of overfitting.

• Some of the best-supervised learning algorithms of classification.

• How to work with non-linear classification models.

• The different validation and optimization techniques that you can use to make sure your algorithms respond the way that you want them to.

• Moving on to some unsupervised machine learning that we can use, and the best clustering algorithms along the way.

• A look at the Principal Component Analysis and the Linear Discriminant Analysis and how they compare to one another.

All you have to do now is scroll the top of the page and select the »» BUY NOW «« button to start your journey !