Multilayer Perceptron Classifier in Python: Interactive Guide with a Neuron Lab
Learn the multilayer perceptron by playing: tune a single neuron to solve AND and OR, discover why XOR needs hidden layers, activations and backpropagation.
Blending Classifier in Python: Interactive Guide
Learn blending by playing: hold-out validation sets, base models and the blender, how blending differs from stacking, and leak-free Python code.
Evaluation Metrics for Classification Models: Interactive Guide
Learn the confusion matrix, accuracy, precision, recall, specificity, F1, MCC, log loss and ROC AUC by playing with a live threshold and ROC curve on real data.
Ensemble Learning: Interactive Guide to Bagging, Boosting, Stacking and Blending
Learn ensemble learning by playing: a wisdom-of-the-crowd vote simulator, bagging vs boosting vs stacking vs blending, and weak vs strong learners.
Decision tree classification in R
Decision Tree Classification in R: Interactive Guide with a Depth Slider
Learn decision trees by playing: entropy, Gini and information gain, ID3 vs C4.5 vs CART, and a live depth slider that shows overfitting, with rpart code in R.
Elastic Net Regression in R: Interactive Guide with an α–λ Playground
Learn Elastic Net regression in R by playing: mix Ridge and Lasso with alpha, slide lambda on real data, then fit and tune it with glmnet.
Ridge Regression in R: Interactive Guide with a λ Playground
Learn ridge regression in R by playing: a live λ playground on mtcars, Ridge vs Lasso rapid fire, glmnet code and a final boss quiz.
Linear Regression in Python: Interactive Guide with a Line-Fitting Game
Learn linear regression by fitting the line yourself: drag the slope and intercept, do the maths, check the assumptions and code it in Python.
Stepwise Regression in Python: Interactive Guide with Forward Selection
Learn stepwise regression by playing forward selection on real data: pick features by p-value, compare forward, backward and bidirectional methods, AIC and BIC.
Generalized Linear Regression (GLM) in Python: Interactive Guide
Learn generalized linear models by playing: the three GLM components, matching outcomes to Normal, Binomial, Poisson and Gamma families, and a link-function lab.