Machine Learning Foundations
Mathematical foundations, learning theory, supervised and unsupervised methods, neural networks, and production ML systems.
Start Module 01Curriculum
A structured path through the course content.
Mathematical Foundations
Linear algebra, calculus, probability, and statistics for ML.
Data Science Fundamentals
Data exploration, cleaning, and preparation.
Core Learning Theory
Bias-variance tradeoff, PAC learning, and generalization.
Supervised Learning: Regression
Linear regression, regularization, and regression techniques.
Supervised Learning: Classification
Logistic regression, SVMs, decision trees, and classification.
Ensemble Methods
Bagging, boosting, random forests, and model ensembles.
Unsupervised Learning
Clustering, dimensionality reduction, and anomaly detection.
Neural Network Foundations
Perceptrons, backpropagation, and deep learning basics.
Probabilistic Methods
Bayesian methods, graphical models, and probabilistic inference.
Model Selection & Evaluation
Cross-validation, metrics, and model comparison.
Feature Engineering
Feature creation, selection, and transformation techniques.
ML Systems & Production
MLOps, deployment, monitoring, and production ML.