Lesson 300
Machine Learning Foundations
Learn from data, not rules
1:00How machine learning flips the programming model — learning rules from data and answers — covering supervised, unsupervised, and reinforcement paradigms, the train/test pipeline, regression, loss minimization via gradient descent, and the overfitting/underfitting tradeoff.
By the end, you can
- Explain the difference between traditional programming and machine learning in terms of what goes in and what comes out.
- Distinguish supervised, unsupervised, and reinforcement learning by their defining characteristics.
- Identify features and labels in a tabular dataset.
- Describe why a train/test split is necessary and state a typical ratio.
- Classify a given prediction task as regression or classification.
- Explain what a residual is and why MSE squares the errors.
- Describe how gradient descent minimizes the loss, and what happens when the learning rate is too large.
- Diagnose underfitting vs overfitting from training and test accuracy.
- Explain the bias-variance tradeoff and name two tools for finding the sweet spot.
- Articulate why high test accuracy does not mean a model "understands" the problem.
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