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While there is a lot that you are able to with traditional forms of coding, it isn’t able to meet all of your needs. What if we were able to make a program that was able to learn on its own? What if we could put in a bit of information, and the program were able to do what it needed to, and take control. This is where the beauty of machine learning is going to come into play!
Some of the topics that we are going to explore with Python machine learning inside this guidebook include:
The different types of machine learning that you are able to work with.
The difference between machine learning and deep learning.
How to set up and use the Scikit-learn library from Python.
How to set up and use the TensorFlow library.
The K-Nearest Neighbors and the K-Means clustering algorithms.
How to use support vector machines with machine learning.
Working with neural networks and recurrent neural networks.
How decisions trees can help you make smarter decisions, and turning these decision trees into random forests.
Working with linear classifiers when you are in machine learning.
There are so many things that we are able to work with when it comes to machine learning, and the field is going to grow in leap and bounds through the years.
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