intermediate Level Self-Paced Track Certificate Included

Foundations of Machine Learning

Code-first principles of supervised and unsupervised machine learning. Build linear regressors, vectorized gradient descent optimizers, and regularization pipelines from mathematical scratch.

1h 50m total content
5 lessons & projects
Instructor: Atul Jha
Foundations of Machine Learning

Course Overview

About this Course

A rigorous, mathematically grounded, and implementation-driven curriculum covering the fundamentals of machine learning algorithms.

Key Competencies You'll Build

Mathematical foundations written from pure first principles
Clean, reproducible PyTorch / Python code without black boxes
Production deployment and low-latency inference patterns
Hands-on capstone portfolio piece ready for GitHub

Course Curriculum

1 chapters • 5 total lessons & checkpoints

ArticleThe Machine Learning Paradigm: From Rules to Representations
18 minStart →
ArticleOrdinary Least Squares & Closed-Form Normal Equation
24 minStart →
VideoGradient Descent, Convex Optimization & Learning Rates
22 minStart →
ArticleRidge, Lasso, ElasticNet & The Bias-Variance Tradeoff
21 minStart →
LabInteractive Loss Surface & Gradient Descent Optimizer Lab
25 minStart →