📄️ K-Nearest Neighbors
Understanding the proximity-based classification algorithm: distance metrics, choosing K, and the curse of dimensionality.
📄️ Logistic Regression
Understanding binary classification, the Sigmoid function, and decision boundaries.
📄️ Support Vector Machines
Mastering the geometry of classification: margins, hyperplanes, and the Kernel Trick.
📄️ Decision Trees
Understanding recursive partitioning, Entropy, Gini Impurity, and how to prevent overfitting in tree-based models.
📄️ Random Forest
Understanding Ensemble Learning, Bagging, and how Random Forests reduce variance to build robust classifiers.
📄️ Gradient Boosting
Exploring the power of Sequential Ensemble Learning, Gradient Descent, and popular frameworks like XGBoost and LightGBM.