Open-Source Learning Tools

Machine Learning Explained

Interactive tools for understanding ML algorithms step by step. Pick a topic and start exploring.

ML Lectures SS2026 · HKA / EIT

Data Preparation And Feature Engineering

Complete pipeline from data types through EDA (descriptive stats, box plots, scatter plots, segmentation) to imputation, scaling, encoding, and feature selection — all with the Heart Failure dataset.

ML_02 / ML_03 EDA Preparation Interactive

Linear Regression

Simple and Multiple Linear Regression Deployment

Evaluation Metrics Regularization Grid Search and K-Fold Cross Validation

Bias And Variance Tradeoff

Bias–Variance & Generalization Explorer: Inspired by Raschka, STAT 479: Model Evaluation 1 – Overfitting and Underfitting

Overfitting vs. underfitting Bias–variance decomposition of the squared loss S. Raschka

Grid Search & K-Fold Cross Validation

Discover how GridSearchCV from Scikit-Learn calculates the mean and standard deviation of validation folds to find the best α for a Lasso regression Model, and how refit=True fully leverages your training data.

Gridsearch K-Fold Cross Validation Scikit-Learn

Sklearn-Pipeline

Explaining How can sklearn pipeline Chaining preprocessing steps and a model into a single, reproducible workflow.

Preprocessing Pipelines Scikit-Learn

SMOTE & SMOTE-NC

Step-by-step walkthrough of synthetic oversampling for imbalanced datasets. See every distance calculation, k-NN selection, and synthetic sample generation live.

Imbalanced Data Oversampling Chawla et al.

Attention Is All You Need

Interactive guide to the Transformer architecture — self-attention, Q/K/V matrices, multi-head attention, positional encoding, masking, and the full encoder-decoder pipeline with step-by-step math.

Vaswani et al. 2017 Self-Attention Transformers