Publisher's Synopsis
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What if you could make your program one that can learn by trial and error or based on the information you show?
What if you could get a program that could adapt and change based on the user's input?
And what if you were able to make all of this happen with the Python coding language, helping beginner's work with more complicated codes?
This is all possible with Python machine learning.
This guidebook will take some time to look at Python machine learning and all of the neat things that you can do with it.
Machine learning is a growing field that many 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 Python coding language's help, anyone can start writing their codes in machine learning.
This guidebook will take a look at all of the different topics that you need to know 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 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 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!
If all of this sounds like your ideal book, hop on over and hit now that buy button!
