Course · 4 ways to learn

Machine Learning

Build real intuition for ML — play with 130+ interactive visualizations, work through guided derivations, and read the written handbook. Regression to gradient boosting and neural nets.

Who it's for

Anyone who wants to *see* how ML works rather than just read the math — learners, bootcampers, and engineers refreshing the fundamentals.

What you'll learn

  • Build visual intuition for how models learn from data
  • Derive core algorithms — regression, SVMs, trees, boosting — by hand
  • Read evaluation tools fluently — confusion matrix, ROC/AUC, cross-validation
  • Connect bias–variance, regularization, and tuning into one workflow

Course contents

  1. 01Foundations — features & labels, train/test split, the workflow
  2. 02Core algorithms — regression, kNN, k-means, trees, SVM
  3. 03Ensembles & nets — random forests, gradient boosting, neural nets
  4. 04Evaluation & techniques — metrics, PCA, regularization, tuning