Machine Learning with Python
A hands-on course focused on building and evaluating models in Python using scikit-learn and Jupyter. Covers regression (linear, multiple, polynomial, logistic), supervised learning (decision trees, k-NN, SVM), and unsupervised techniques (clustering) plus dimensionality reduction (PCA, t-SNE, UMAP). Emphasizes data preparation, metrics, cross-validation, regularization, and pipeline optimization, culminating in a rainfall-prediction project and exam.