Overview

The world of machine learning is changing all the time. It is so amazing the idea that we are able to take a computer and let it learn as it goes. Without having to write out all of the codes that we need for every situation out there or every input that the user may pick, we are able to write out codes in machine learning, even with Python, in order to let the computer or device learn and make decisions on its own. This guidebook is going to take a closer look at how Python machine learning is able to work, as well as how you can use some of the tools and techniques that come with this process for your own needs. When you are interested in learning more about what machine learning is all about, as well as how you can use a part of the coding from Python inside of this process, then this guidebook is the tool for you! Some of the topics that we will explore when we go through this guidebook will include: - Understanding some of the basics of machine learning; - Some of the different parts that you need to know to get started with machine learning and the Python language; - Understanding the Scikit-Learn library, and why it is so important to work with this type of library; - How to work with the K-Nearest Neighbors algorithm; - What are support vector machines, random forest algorithm, and recurrent neural networks; - What are linear classifiers; - How K-Means clustering is going to be different from KNN; - Other great things that you are able to do with Python Machine Learning. The field of machine learning is growing exponentially-and with the help of Python and all of the cool tools and libraries that come with it, you will find that there are endless possibilities of what you will be able to do with it. When you are ready to learn more about Python Machine Learning and when you want to be able to work towards your own projects and applications with this cool topic, make sure to check out this guidebook to help you get started! Scroll to the top of the page and select the buy now button!

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9781802290271

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