Machine Learning Basics: Supervised v Unsupervised

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AI and machine learning can help transform a massive pile of data into useful insights. Understanding which branch of machine learning to use - supervised or unsupervised - is key to getting the most impactful analysis. IBM’s Mark Sturdevant identifies the key differences and explains concepts like clustering, regression analysis, and dimensionality reduction.
00:00 - Introduction
00:15 - Differences between supervised and unsupervised machine learning
1:02 - Supervised machine learning examples: binary classification, multi-class classification, and regression
3:13 - Unsupervised machine learning examples: clustering, association, and dimensionality reduction
5:05 - Which approach is right for you?
5:43 - Resources to help you get started

[@khurram-malik]

Thanks for simpel explanation

[@habibullahrahat1649]

Thanks IBM ❤

[@jonathanchan7682]

fantastic, simple to differentiate

[@TechnoAllArab]

great overview of AI and machine learning, Thank you

[@sitrakaforler8696]

Great vidéo! Data Camp also made a great vidéo about it but yours is free ^^ Keep it up in that Data/ML Litteracy ! bravo IBM and its workers!!!!

[@habibullahrahat1649]

How much “Statistics” or “Statistical learning” knowledge should a data scientist need to know…??

[@armanrangamiz3813]

It was awesome. Thank you.

[@dembobademboba6924]

Thank you. It is very helpful

[@natural-song]

IBM is awesome and gread!

[@shadepb]

wow!