Machine Learning, Visualised
Classifiers, regressors, clustering and the evaluation metrics that tell you whether any of it worked.
About this track
A machine learning model is easier to trust once you have watched it get things wrong. Every module in this track puts the model on screen with the controls that drive it, so you can push it into failure on purpose: set k to 1 and watch KNN overfit, unbalance the classes and watch accuracy stay high while the model becomes useless.
The track runs from splitting data through the classical algorithms - linear regression, KNN, decision trees, naive Bayes, SVM, k-means - and ends on the evaluation and drift questions you hit once a model is real.
All 19 modules, in teaching order
- 01Train-Test Split MethodSee why testing on data you trained on flatters a model, and how holding out a test set gives you an honest score.
- 02Label Encoding ProcessTurn categories into integers, and see where label encoding is safe and where it invents an ordering your model will believe.
- 03One-Hot EncodingExpand a categorical column into binary columns and see how one-hot encoding avoids implying an order that was never there.
- 04Linear Regression with OLSDrag a regression line by hand and watch the residuals and mean squared error react, then let least squares find the optimum.
- 05Evaluation Metrics for RegressionCompare MAE, MSE, RMSE and R-squared on identical predictions and see which kinds of error each metric punishes hardest.
- 06K-Nearest NeighborsDrop a new point on the plane and watch KNN classify it by neighbour vote. Change k to see the decision boundary tighten or smooth.
- 07Confusion Matrix AnalysisSee why accuracy alone misleads. Build a confusion matrix cell by cell and watch precision, recall and F1 shift with every prediction.
- 08Decision Tree AnalysisWatch a decision tree choose each split by information gain, growing branch by branch until every leaf holds a single class.
- 09Naive Bayes ClassifierStep through Bayes' theorem on real text and see why a 'naive' independence assumption still classifies spam remarkably well.
- 10Support Vector MachinesAdd points from two classes and watch a support vector machine find the hyperplane with the widest possible margin between them.
- 11K-Means ClusteringPlace centroids and watch K-Means alternate between assigning points and recentring clusters until it converges.
- 12Cosine Similarity MetricMeasure similarity by angle instead of distance. Drag two vectors apart and watch the cosine similarity score respond in real time.
- 13K-Fold Cross ValidationWatch data split into k folds, each taking a turn as validation, and see why a single train/test split can flatter a model.
- 14Bias vs VarianceDial bias and variance up and down, resample the data, and watch a stable underfit turn into an unstable overfit.
- 15Label Imbalance ProblemSee how a model scores 99% accuracy while missing every fraud case, and why imbalance breaks your intuition about metrics.
- 16Training on Imbalanced DatasetTry resampling, class weights and threshold tuning on an imbalanced dataset and watch minority-class recall respond.
- 17Hard vs Soft LabellingCompare a single hard label against a probability distribution, and see what a model loses when forced to pick one winner.
- 18Sliding Window for Time SeriesSlide a window across a time series to turn a raw sequence into supervised training rows of features and targets.
- 19Model and Data DriftFast-forward through production and watch a well-trained model decay as incoming data drifts away from what it learned.