What Is Linear Regression? How Least Squares Works Linear regression fits a straight line through data to predict one quantity from another, and least squares picks the line with the smallest squared errors.
Cross-Validation Resampling techniques for estimating how well a model performs on unseen data, most commonly k-fold cross-validation.
k-Means Clustering An unsupervised learning algorithm that partitions data into k clusters by iteratively assigning points to the nearest centroid.
k-Nearest Neighbors A simple instance-based machine learning algorithm that classifies or regresses by majority vote among the k closest training examples.
Optical Character Recognition The conversion of images of text — scans, photographs, screenshots — into machine-readable text.
Word Embeddings Dense vector representations of words learned from text, in which semantically similar words have similar vectors.
Gradient Descent The iterative optimization algorithm at the heart of training machine learning models: how it works, its variants, and its pitfalls.
Reinforcement Learning How agents learn from rewards and punishments through trial and error: Markov decision processes, value functions, and policy learning.
Decision Trees and Random Forests Interpretable rule-based models built by recursive splitting, and the forests of randomized trees that made them competitive.
Overfitting and Regularization When models memorize instead of generalize, and the standard techniques — from data to penalty terms — that prevent it.