C1
Linear Algebra and Probability for ML
6 lessons3 hours2 of 6 publishedFree
Outcome
Use vectors, matrices, and probability distributions in code; apply Bayes’ rule and maximum likelihood.
Prerequisites
- High school math, basic Python.
Lessons
- L01Vectors and dot productsWhat vectors are, how to add and scale them, and how the dot product measures length, angle, and the similarity between embeddings.
- L02Matrices as transformations · coming soonExercise: Rotate and scale points, then plot.
- L03Eigenvalues and SVD · coming soonExercise: Compress an image with SVD.
- L04PCA from scratch · coming soonExercise: Reduce to two dimensions and report variance kept.
- L05Probability and Bayes' rule · coming soonExercise: Simulate a medical test and check the answer.
- L06Distributions and MLEProbability distributions as models of data, and maximum likelihood estimation: fit a Gaussian analytically, then numerically, and see why both agree.