Python Machine Learning: The Intensive Course For Beginners In Programming, Concepts, Tools And Techniques For Building Intelligent Systems

Unknown Author

ISBN 10: 1914916433 / ISBN 13: 9781914916434

Published by Charlie Derek

About this Item

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Bibliographic Details

• Title: Python Machine Learning: The Intensive Course For Beginners In Programming, Concepts, Tools And Techniques For Building Intelligent Systems
• Publisher: Charlie Derek
• Language: English
• ISBN 10: 1914916433
• ISBN 13: 9781914916434
• Binding: Soft cover
• Condition: VeryGood

About this title

Synopsis

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If you are looking for a comprehensive guide that explains in a simple way how to manage machine learning and AI, please keep reading.

What if you could make your own program, one that is able to learn by trial and error, or based on the information that you show it?

What if you could get a program that could adapt and change based on the input of the user? And what if you were able to make all of this happen with the Python coding language, helping even beginner's work with more complicated codes? This is all possible with Python machine learning.

 

This guidebook is going to take some time to look at Python machine learning and all of the neat things that you are able to do with it. Machine learning is a growing field, one that a lot of programmers want to spend their time on.

But even though this sounds like a complicated part of technology to work with, you will find that with the help of the Python coding language, anyone can start writing their own codes in machine learning.

 

This guidebook is going to take a look at all of the different topics that you need to know in order to get started with Python machine learning. Some of the topics that we will explore inside include:

• The basics of machine learning
• The difference between supervised and unsupervised machine learning.
• Setting up your new environment in the Python language.
• Data preprocessing with the help of machine learning.
• How to use Python coding to help with linear regression.
• Decision trees and random forests.
• How to work with support vector regression problems.
• Can machine learning really help with Naïve Bayes problems?
• Accelerated data analysis using the Python code.
• And so much more!

If you have been interested in learning more about machine learning, and you want to be able to learn a few of the codes that can make it happen for you, make sure to check out this guidebook to help you get started!