Models explained like engineering tools not buzzwords.
Understand what each model does, when it is useful, where it fails, what to measure and how it behaves in production.
Logistic Regression
A probabilistic classification baseline that is transparent, fast and surprisingly competitive.
Random Forest
An ensemble of decorrelated decision trees that captures nonlinear interactions with limited preprocessing.
Gradient Boosting
Sequential trees correct residual errors and often dominate structured-data benchmarks.
K-Means
Partitions observations around centroids and provides a simple baseline for segmentation.
PCA
Rotates data into orthogonal directions that capture maximum variance.
Multilayer Perceptron
A stack of learned affine transformations and nonlinearities that approximates complex functions.
Convolutional Neural Network
Learns local spatial filters and hierarchical visual features.
Transformer
Uses attention to model relationships between tokens without recurrent state.
BM25
A strong lexical ranking algorithm balancing term frequency, rarity and document length.
TF-IDF
Represents text by upweighting terms frequent in a document but rare across the corpus.
Q-Learning
An off-policy temporal-difference method that learns action values from reward transitions.
Contextual Bandit
Chooses among actions using context while learning from immediate reward.
Thompson Sampling
Samples action quality from posterior beliefs to balance exploration and exploitation.
Markov Decision Process
A formal model of states, actions, transition probabilities, rewards and discounting.
Naive Bayes
Uses Bayes rule with conditional independence assumptions for fast classification.
Support Vector Machine
Finds a maximum-margin decision boundary and can use kernels for nonlinear separation.
Isolation Forest
Detects anomalies by how quickly random trees isolate a point.
ARIMA
Models autoregressive structure, differencing and moving-average residuals.
XGBoost
Regularized gradient boosting engineered for strong tabular performance and efficient training.
Graph Neural Network
Learns node/edge representations by passing messages across graph neighborhoods.