Introduction to AI & Machine Learning

Understand what AI and ML really are, the types of machine learning, and the end-to-end ML workflow.

Learn your first ML algorithm: predicting numbers with the line of best fit using Scikit-Learn.

Extend linear regression to handle multiple input features, interpret coefficients, and model non-linear curves with polynomial features.

Learn to predict categories instead of numbers using logistic regression, the sigmoid function, and decision boundaries.

Learn the intuitive KNN algorithm: classify data by finding its closest neighbors using distance metrics.

Go beyond accuracy: learn confusion matrices, precision, recall, and F1-score to properly evaluate classification models.

Transform raw data into model-ready features: scaling, encoding categorical variables, and feature selection.

Learn how decision trees make predictions through if/else splits, visualize tree logic, and understand overfitting.

Combine many decision trees into a powerful ensemble: learn bagging, random forests, and feature importance.

Discover hidden patterns in unlabeled data using K-Means clustering and the elbow method.