ML Methods
Classical machine learning worked through one algorithm at a time: what it assumes, where it breaks, and a runnable example for each.
This is the pre-deep-learning half of machine learning, the part that still solves most problems that arrive as a table of numbers. Each page takes one algorithm, states the intuition in a line, lists what it is good and bad at, then works an example in scikit-learn.
Several algorithms get two pages: one for the method and one marked TUT applying it to a real dataset. Read them as a pair.
For neural networks see DL Methods, and for forecasting see Time Series.
Foundations
The parts that apply no matter which model you pick.
| Page | Covers |
|---|---|
| Training and Testing | Why you hold data back at all, and how train_test_split does it |
| Loss and Cost Functions | The common losses, and which ones suit classification against regression |
| L1 and L2 Regularization | Overfitting shown on a plain regression, then Lasso and Ridge as the fix |
| Hyper-parameter Optimization | Three escalating approaches: manual trial and error over a split, then K-fold cross validation, then GridSearchCV |
Regression
Predicting a continuous value.
| Page | Covers |
|---|---|
| Linear Regression | The single-variable case, ending in generated house price predictions |
| Multi-Linear Regression | The same with several input variables |
| Polynomial Regression | Fitting curves, second order and higher |
| Kernel Ridge Regression | Ridge regression combined with the kernel trick, for non-linear relationships |
| Support Vector Regression | SVM applied to a continuous target |
Classification
Predicting a category.
| Page | Covers |
|---|---|
| K-NN | Take any point, it is what it is close to. Distance metrics, choosing k, then a first model and a GridSearchCV search for the optimum |
| Decision Tree | If-statements placed where entropy drops fastest, plus the trade-offs of CART |
| Logistic Regression | Binary and multiclass, and the classification types each suits |
| Naive Bayes | The Bayesian approach and Gaussian processes |
| Naive Bayes Tutorial | The same method built into an email spam filter |
| Random Forest | Many trees voting |
Ensembles
| Page | Covers |
|---|---|
| Ensemble Methods | Bagging against boosting, and when each helps |
| Ensemble Tutorial | Bagging applied to heart disease prediction, then boosting |
| Bagging: Diabetes Prediction | A second worked bagging example |
Unsupervised Learning
Finding structure without labels.
| Page | Covers |
|---|---|
| Principal Component Analysis | Dimensionality reduction |
| PCA Tutorial | PCA applied to heart disease prediction |
| Gaussian Mixture Models | Modelling data as a mixture of Gaussians, the expectation-maximization algorithm behind it, and its use for clustering, density estimation, and anomaly detection |
| K-Means Clustering | The algorithm step by step, choosing K, and the alternatives when it does not fit |
GPU Programming
| Page | Covers |
|---|---|
| CUDA | The odd one out, and the longest page here. Getting a CUDA toolchain working, then writing kernels: RGB to greyscale, then matrix multiplication. Useful background for why the deep learning libraries are shaped the way they are |
Not Covered Yet
- Three pages are placeholders marked “coming soon” with no content: CNNs, Generative Models, and Back Propagation. All three are really deep learning topics and are covered properly in DL Methods.
- Nothing on gradient boosting libraries. No XGBoost, LightGBM, or CatBoost page, despite them being the usual winners on tabular data. XGBoost is covered from the forecasting angle in Time Series.
- No feature engineering page, and no treatment of missing data, categorical encoding, or scaling as topics in their own right.
- No model interpretability: nothing on SHAP, permutation importance, or partial dependence.
- Evaluation is spread through the tutorials rather than collected anywhere, so there is no single page on precision, recall, ROC, or calibration.