Learn AISimple models and useful baselines
Compare neighbors, fitted trees, numerical regression and class probabilities with simple references before adding complexity.
What you’ll explore
- Baselines
- Nearest neighbors
- Decision trees
- Linear regression
- Logistic regression
- Random forests
- Boosting
- Clustering
- PCA
- Anomaly detection
- Recommendation
Your lessons
0 / 10 completeStart with a simple baseline
Fit majority, nearest-neighbor and shallow-tree predictors; compare the same validation cases before adding complexity.
Fit a line and explain its errors
Move a line over licensed flower measurements, inspect residuals and compare a fitted model with a mean baseline and shuffled labels.
Predict a class probability
Train a two-feature probability model, inspect its gradients and compare estimates with outcomes before choosing an action threshold.
Combine many imperfect trees
Fit bootstrap trees, inspect their votes and out-of-bag coverage, and test whether shared mistakes survive a measurement shift.
Correct residual errors with boosting
Fit small trees to the mistakes left by earlier predictions, inspect each correction and choose complexity using separate development evidence.
Find structure without labels
Alternate k-means assignments and center updates, then project onto a principal axis and inspect distinctions that vanish.
Find unusual examples
Fit a reference-distance detector, inspect unseen normal behavior and count false alerts when confirmed problems are rare.
Recommend from incomplete feedback
Complete a small ratings picture with predictions, compare simple and learned recommenders, and inspect cold start and exposure bias.
Validate without reusing the answers
Inspect cross-validation folds, repair duplicate and time leakage, and fit preprocessing inside the training boundary.
Finish a tabular experiment
Turn a real-data comparison into a frozen, reproducible report with error examples and carefully bounded claims.