Stanford CS229 Machine Learning (Andrew Ng): study plan and how long it takes
The Stanford Online playlist is 21 videos and 27.9 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.
21 videos · 27.9 hours · Stanford Online
CS229 is Stanford's graduate-level machine learning course. Unlike Andrew Ng's beginner-friendly Coursera courses, it derives the algorithms mathematically: you'll see the probability and linear algebra behind regression, SVMs, EM and reinforcement learning.
Every lecture is about 80 minutes. The maths is the bottleneck, not the runtime, so plan fewer lectures per week than the hours suggest and keep time for working through the derivations.
Who it's for: Learners with linear algebra, probability and multivariable calculus who want the theory behind classical machine learning, not just how to call a library.
How long does it take?
The full playlist is 27.9 hours across 21 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:
| Daily time | At 1x | At 1.5x | At 2x |
|---|---|---|---|
| 30 min a day | 56 days | 38 days | 28 days |
| 1 h a day | 28 days | 19 days | 14 days |
| 2 h a day | 14 days | 10 days | 7 days |
Make your own schedule
The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.
Week-by-week schedule
A sample plan at 1 hour a day and normal speed, using whole videos so none is split across days. That's 3 weeks in total. For a different pace, start date or speed, use the planner above.
Week 1Videos 1–7 · 9h 15m
Day 1 · 1h 15m
- 1Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018)1h 15m
Day 2 · 1h 18m
- 2Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)1h 18m
Day 3 · 1h 20m
- 3Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018)1h 20m
Day 4 · 1h 22m
- 4Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018)1h 22m
Day 5 · 1h 19m
- 5Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)1h 19m
Day 6 · 1h 21m
- 6Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)1h 21m
Day 7 · 1h 20m
- 7Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)1h 20m
Week 2Videos 8–14 · 9h 26m
Day 8 · 1h 23m
- 8Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018)1h 23m
Day 9 · 1h 26m
- 9Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018)1h 26m
Day 10 · 1h 21m
- 10Lecture 9 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)1h 21m
Day 11 · 1h 20m
- 11Lecture 10 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)1h 20m
Day 12 · 1h 17m
- 12Lecture 11 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018)1h 17m
Day 13 · 1h 19m
- 13Lecture 12 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018)1h 19m
Day 14 · 1h 21m
- 14Lecture 13 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)1h 21m
Week 3Videos 15–21 · 9h 10m
Day 15 · 1h 20m
- 15Lecture 14 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn20181h 20m
Day 16 · 1h 19m
- 16Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 20181h 19m
Day 17 · 1h 18m
- 17Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018)1h 18m
Day 18 · 1h 19m
- 18Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning Andrew Ng (Autumn2018)1h 19m
Day 19 · 1h 20m
- 19Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018)1h 20m
Day 20 · 1h 21m
- 20Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018)1h 21m
Day 21 · 1h 13m
- 21RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018)1h 13m
Study plan, phase by phase
Video numbers are positions in the playlist. To schedule just one phase, enter its numbers in the planner's From and To fields.
- 01
Supervised learning foundations
Videos 1–5 · 6h 34m
Introduction, linear regression and gradient descent, locally weighted and logistic regression, the perceptron and generalized linear models, then GDA and Naive Bayes.
- 02
SVMs, kernels and model selection
Videos 6–9 · 5h 31m
Support vector machines, kernels, data splits and cross-validation, and a discussion section on learning theory.
- 03
Trees, neural networks and debugging
Videos 10–13 · 5h 16m
Decision trees and ensembles, an introduction to neural networks, backprop, and Andrew Ng's lecture on debugging ML models and error analysis, which is useful on its own.
- 04
Unsupervised learning
Videos 14–16 · 3h 59m
The EM algorithm, factor analysis, PCA and ICA.
- 05
Reinforcement learning
Videos 17–21 · 6h 31m
ICA wraps up and reinforcement learning begins: MDPs, value and policy iteration, continuous-state MDPs, linear dynamical systems, and RL debugging.
Tips for finishing
- Plan two or three lectures a week, not one a day. Each lecture needs time with the lecture notes from the course website.
- Lecture 12, on debugging ML models and error analysis, is the most practical in the course. Watch it even if you skip others.
- If the maths feels heavy, watch 3Blue1Brown's Essence of Linear Algebra first. It's only 3 hours.