Logistic Regression
Sigmoid function, binary cross-entropy, gradient descent, and regularization.
Linear Regression
OLS, cost functions, normal equation, R-squared, and gradient descent optimization.
Neural Networks
Perceptrons, backpropagation, activation functions, and multi-layer architectures.
Decision Trees
Entropy, Gini impurity, random forests, gradient boosting, and feature importance.
Naive Bayes
Bayes' theorem, Gaussian/Multinomial/Bernoulli variants, Laplace smoothing, and text classification.
Support Vector Machines
Maximum margin classifier, kernel trick, hyperplanes, and support vectors.
K-Nearest Neighbors
Distance metrics, lazy learning, weighted voting, and the curse of dimensionality.
K-Means Clustering
Centroid-based clustering, elbow method, silhouette score, and unsupervised learning.
Gradient Boosting
Sequential ensemble boosting, XGBoost, learning rate, feature importance, and regularization.
PCA
Dimensionality reduction, eigendecomposition, variance maximization, and data reconstruction.
DBSCAN
Density-based clustering, epsilon neighborhoods, core/border/noise points, and parameter tuning.
Random Forest
Ensemble of decision trees, bootstrap aggregating, feature importance, and out-of-bag error.
CNN
Convolutional neural networks, feature maps, pooling, transfer learning, and image classification.
RNN / LSTM
Recurrent neural networks, LSTM gates, vanishing gradients, sequence modeling, and GRU.
Transformers
Self-attention mechanism, multi-head attention, positional encoding, BERT, GPT, and scaling laws.