Machine Learning
by Andrew Ng · Stanford Online
Our Verdict
Worth it — with caveatsStanford CS229 (Stanford Online / Stanford Engineering Everywhere), the graduate-level Machine Learning course created by Andrew Ng, is the gold-standard free option for learners who want the mathematics behind ML rather than just an API tour. The most-cited free version is the Autumn 2018 lecture series taught by Andrew Ng (20 lectures on YouTube), backed by the canonical CS229 lecture notes and four problem sets covering supervised learning, learning theory, unsupervised learning, and reinforcement learning. It is rigorous and unforgiving: it assumes comfort with linear algebra, multivariable calculus, probability, and Python/NumPy, and offers no certificate, grading, or hand-holding when self-studied. Take it if you want first-principles depth (derivations, GLMs, EM, SVM duality); skip it if you want a gentle, applied, project-first introduction.
Outstanding, free, mathematically rigorous ML foundation from Stanford and Andrew Ng, but only worthwhile if you already have solid linear algebra, calculus, probability, and Python; absolute beginners will struggle and there is no official certificate.
Best for: CS/engineering students and working developers who already know linear algebra, multivariable calculus, and probability, want to understand the math behind ML algorithms (not just call libraries), and are comfortable self-pacing through derivations and problem sets. Ideal as preparation for research, deep-learning courses (CS231n/CS224n), or interviews that probe ML fundamentals.
Skip if: Complete beginners, non-technical professionals, or anyone wanting a gentle, applied, project-first path. People who need a certificate, graded feedback, deadlines, or hand-holding, or who are rusty on linear algebra/probability, should start elsewhere (Coursera ML Specialization, fast.ai, or Google ML Crash Course) first.
About This Course
Stanford's foundational machine learning course covering supervised learning, unsupervised learning, and best practices in ML.
What You'll Learn
Curriculum
Course motivation, supervised-learning setup, and linear regression with least squares.
Weighted least squares, logistic regression, Newton's method, perceptron, and generalized linear models.
Gaussian Discriminant Analysis, Naive Bayes, and Laplace smoothing.
SVM formulation, the kernel trick, and margins.
Bias-variance tradeoff, regularization, and model/feature selection.
Decision trees, ensembles, and neural-network basics plus training.
Debugging learning algorithms and applying ML to real projects.
K-means, mixture of Gaussians and EM, factor analysis, PCA, and ICA.
MDPs, Bellman equations, value/policy iteration, LQR/LQG, Q-learning, and policy search (REINFORCE, POMDPs).
Four graded-style problem sets (plus PS0) with data files and solutions, including derivations like the exponential-family mean/variance and Hessians.
Prerequisites
- Linear algebra (vectors, matrices, eigenvalues) — MATH51 / CS205L level
- Multivariable calculus (gradients, Hessians, partial derivatives)
- Probability and statistics — CS109 / MATH151 level
- Programming able to write non-trivial Python/NumPy code
- Computer-science fundamentals at CS106A/B level
Instructor
Andrew Ng
Instructor · Stanford Online
Pros & Cons
Pros
- Genuinely rigorous: teaches the mathematical derivations behind ML (GLMs, EM, SVM duality, RL), not just library usage — the depth its Coursera counterpart lacks.
- Completely free: all 20 Autumn-2018 lectures stream on YouTube, with lecture notes, problem sets, and solutions plus linear-algebra/probability refreshers via Stanford Engineering Everywhere.
- Authoritative source: created and taught by Andrew Ng (co-founder of Coursera and Google Brain); CS229 is Stanford's flagship ML course and a long-standing field reference.
- Broad, coherent coverage of supervised, unsupervised, and reinforcement learning plus a dedicated lecture on debugging/applying ML in practice.
- Self-paced with no deadlines or fees when studied via the public materials.
Cons
- Steep prerequisites and difficulty: official problem sets demand things like deriving the exponential family's mean/variance and computing Hessians; historically hard exams (one cited median ~46/100). Rusty math means a hard wall.
- No certificate, grading, or instructor feedback for self-learners — you must self-assess against provided solutions.
- Theory-first and light on modern hands-on tooling/large projects; it is not an applied, portfolio-building course.
- Fragmented free experience: the public version is YouTube videos + PDF notes; the live cs229.stanford.edu site restricts current materials to Stanford-login affiliates, and the canonical free run is Andrew Ng's 2018 edition (current quarters are taught by other faculty).
Alternatives To Consider
Frequently Asked Questions
Is Machine Learning free?
Yes — Machine Learning is free to access. Free. All 20 Autumn-2018 lectures are on YouTube; Stanford Engineering Everywhere provides the videos, canonical lecture notes, four problem sets with solutions, and math refreshers at no cost. No official certificate is offered for the free materials. The current cs229.stanford.edu site shares live syllabus/materials only with Stanford-affiliated logins; a paid graduate-credit/professional version (CS229 / XCS229) exists separately through Stanford.
Who is Machine Learning for?
CS/engineering students and working developers who already know linear algebra, multivariable calculus, and probability, want to understand the math behind ML algorithms (not just call libraries), and are comfortable self-pacing through derivations and problem sets. Ideal as preparation for research, deep-learning courses (CS231n/CS224n), or interviews that probe ML fundamentals.
What will you learn in Machine Learning?
Supervised learning: linear regression, weighted least squares, logistic regression, Newton's method, perceptron; Generalized Linear Models and the exponential family, plus generative models (Gaussian Discriminant Analysis, Naive Bayes); Support Vector Machines, kernels, and the kernel trick; Learning theory: bias-variance tradeoff, regularization, and model/feature selection.
What are the prerequisites for Machine Learning?
Linear algebra (vectors, matrices, eigenvalues) — MATH51 / CS205L level; Multivariable calculus (gradients, Hessians, partial derivatives); Probability and statistics — CS109 / MATH151 level; Programming able to write non-trivial Python/NumPy code; Computer-science fundamentals at CS106A/B level.
Is Machine Learning worth it?
Outstanding, free, mathematically rigorous ML foundation from Stanford and Andrew Ng, but only worthwhile if you already have solid linear algebra, calculus, probability, and Python; absolute beginners will struggle and there is no official certificate.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Stanford Online's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- CS229 official course site (Stanford)
- CS229 Autumn 2018 syllabus (full lecture schedule & problem sets)
- Stanford Engineering Everywhere — CS229 (free videos, notes, psets, no certificate)
- Stanford CS229 Full Course taught by Andrew Ng, Autumn 2018 (YouTube playlist)
- Class Central listing — free Stanford CS229 (Andrew Ng, Autumn 2018)
- Independent learner review — 'Course Review: CS229 by Andrew Ng' (Medium, Abhishek Khurana)
- Learner account of Stanford's AI Professional Program / CS229 difficulty (Lily Chen, Medium)