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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
