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Python Programming: The Ultimate Intermediate Guide to Learn Python Step by Step

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If you're looking to learn about Python, machine learning, and putting it to good use, then keep reading!

Here's the thing: you've tried to make things easier for you in your job, home, or wherever just using classic programming, but there are things you can't do with it. These typically have only a linear structure — it can't learn anything new as any human would. Unfortunately, this is what you need: a program that learns on its own according to the data you feed it — no need to keep reprogramming it to update it every time there's new data! Does this sound like a familiar situation to you?

If it is, then this book has all the answers! It will show you everything you need to know about Python, machine learning, and everything in between. This information allows you to become skilled much faster. Imagine cutting months off of your learning curve and get a strong base of knowledge in no time at all. Imagine getting your project done yourself for a fraction of the cost. This is what this book can do for you.

This is not some phony information written by amateurs, this isn't just a brief insight into python giving you basic knowledge. This is detailed, scientific information compiled together by experts in an easy to read fashion.

In this Python guide, you will discover:

• The importance of Machine Learning
• The Basics of Working with Python
• How to Setting up Your Python Environment
• Data Preprocessing with Machine Learning
• Working with Linear Regression in Machine Learning
• The Benefits of a Decision Tree
• Working on the Random Forest Algorithm
• The Advantages of the Naive Bayes Algorithm
• How Can you Use the KNN Algorithm
…And so much more !

So, if this interests you at all, follow these instructions: click the Buy Now button and start learning Python Machine Learning today!

69 pages, Kindle Edition

Published October 28, 2018

14 people are currently reading
9 people want to read

About the author

Ryan Turner

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Profile Image for Maybelyn Plecic.
39 reviews
November 9, 2018
Good Introduction with Theories

It would have been more helpful if after the code examples were shared that the output was also shared in the book. This is more theoretical than practical.
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