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Home Programming

Applying the Basics of Machine Learning in Python

September 10, 2022
in Programming, Free Course, Tutorials
Applying the Basics of Machine Learning in Python

Applying the Basics of Machine Learning in Python

This Asset we are sharing with you the Applying the Basics of Machine Learning in Python free download links. On our website, you will find lots of premium assets free like Free Courses, Photoshop Mockups, Lightroom Preset, Photoshop Actions, Brushes & Gradient, Videohive After Effect Templates, Fonts, Luts, Sounds, 3d models, Plugins, and much more. Psdly.com is a free graphics content provider website that helps beginner graphic designers as well as freelancers who can’t afford high-cost courses and other things.

File Name:Applying the Basics of Machine Learning in Python
Content Source:https://www.udemy.com/course/applying-the-basics-of-machine-learning-in-python/
Genre / Category:Programming
File Size :518MB
Publisher:udemy
Updated and Published:September 10, 2022
Product Details

This class can serve as you complete beginner’s guide to getting your feet wet in the world of machine learning. Machine learning is certainly the future, but it can definitely be intimidating to get started with. However, not to worry! With this starter-level class, we will go over different aspects such as what machine learning actually is, some of the theory behind it, and finally, we will be creating a project in Python to practice our newly acquired knowledge, where we will try some different algorithms in an open source framework.

This book teaches you how to apply the basics of machine learning in Python. You will learn how to build and evaluate predictive models with the help of real-world data, how to perform various types of regression analysis, and how to identify interesting patterns in your data.

Applying the Basics of Machine Learning in Python is a guide to using machine learning in Python, using easy-to-understand examples and clear instructions. The book covers the different types of machine learning, how to train a model, how to test a model, and more. The author provides a number of code examples that demonstrate how to use different machine learning algorithms in Python.

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