Machine Learning Foundations

Intro · 5 concepts · 5 clips · 7 source videos. Watch the clips in order, then take the quick check to see what did not land.

What kind of problem is this

Labelled data and a target to predict is supervised. Structure to find with no target is unsupervised. Almost every modelling mistake starts by getting this wrong.

IBM separates supervised from unsupervised, then where semi-supervised sits between them (0:33-7:08)

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Source videos · 7

IBM Technology
Short whiteboard explainers of AI infrastructure concepts. Vendor-adjacent, so read the framing critically; the mechanics are sound.
Supervised vs Unsupervised Learning
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DeepLearningAI (Andrew Ng)
Andrew Ng’s course material, posted lecture by lecture. Strongest on the practical diagnosis of a model that is not working.
Bias / Variance (C2W1L02)
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StatQuest (Josh Starmer)
The standard reference for statistics and machine learning fundamentals. Works every idea through a small numerical example rather than a formula.
Machine Learning Fundamentals: Cross Validation
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StatQuest (Josh Starmer)
The standard reference for statistics and machine learning fundamentals. Works every idea through a small numerical example rather than a formula.
Machine Learning Fundamentals: The Confusion Matrix
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Kimberly Fessel
Data scientist. Short, precise treatments of the metrics people routinely confuse.
Precision vs Recall, With a Clear Example
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StatQuest (Josh Starmer)
The standard reference for statistics and machine learning fundamentals. Works every idea through a small numerical example rather than a formula.
A Gentle Introduction to Machine Learning
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StatQuest (Josh Starmer)
The standard reference for statistics and machine learning fundamentals. Works every idea through a small numerical example rather than a formula.
Machine Learning Fundamentals: Bias and Variance
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