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Practice with hands-on code example on how to apply those libraries to demo datasets
Updated on Sep, 2026
Programming, Data Science, Machine Learning
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What is Automated Machine Learning (AutoML)
Will Automated Machine Learning replace Data Scientists? How to use AutoML in Python? What AutoML options are available and free to use?
If you are a beginner and want answers to those questions and to try AutoML yourself, then this course is for you.
Here we go through various Automated Machine Learning (and Deep Learning) frameworks which are currently available (not an extensive list, of course, there are many more).
The main goal is to get an overview of what AutoML is and how to use it in Python. We focus on free AutoML libraries instead of commercial ones so that you can follow along and try them yourself. The course has demo datasets for regression as well as a classification task, so we see both supervised learning tasks for each AutoML library we are going to cover.
Feel free to try out Automated Machine Learning with your data as well
For this course, you should have used Python before (Even AutoML requires us to write a tiny bit of code)
Please also understand what this course is not
A basic introduction to what ML/DL is, or an introduction to Python.
An in-depth theoretical dive into each hyperparameter that can be adjusted/tuned.
An all-in-one solution for every project you want to take in the future.
Hands-on code examples on how to apply those libraries to demo datasets.
Specific relevant information for each library you need to be aware of when you use it.
Helpful tools for any data scientist or business person who wants to reduce redundant and repetitive tasks and free some time to focus on the main steps in the data science life cycle.
Check out the detailed breakdown of what’s inside the course
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