Automated Machine Learning Hands on AutoML for beginners

How to use AutoML in python AutoML in practise What is Automated Machine Learning

What is Automated Machine Learning (AutoML)

What you’ll learn

  • A hands on overview of free python automl packages and how to use them.
  • What is automl.
  • How to use automl in python.
  • Automated machine learning in practise.

Course Content

  • AutoML Introduction Hands on! –> 16 lectures • 2hr 27min.

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Requirements

What is Automated Machine Learning (AutoML)

Will Automated Machine Learning replace Datascientists?

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 try AutoML yourself then this course is for you.

Here we go through various AutomatedMachine 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 libary we are going to cover.

Feel free to try out Automated Machine Learning with your own data as well

For this course you should have used Python before (Even AutoML requires us to write a tiny little bit of code)

 

Please also understand what this course is not

This course does not offer:

A basic introduction to what is ML/DL or an introduction to python

An in-depth  theoretic dive into each hyperparameter which can be adjusted / tuned

An all-in-one solution for every project you want to take in the future

 

This course does offer:

hands on code examples on how to apply those libraries on demo datasets

Specific relevant information for each library you need to be aware of when you use it

Helpful tools for any data scientist of 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 cyclle