The unusually practical third course of Andrew Ng's Deep Learning Specialization on Coursera. It is not about new algorithms but about decision-making: how to set up train/dev/test sets, diagnose what is going wrong, and prioritise where to spend effort. Short but high-value — the kind of judgement that separates working ML engineers from tutorial-followers.
Good for: Learning how to diagnose and prioritise real ML projects.
Less suitable if: You have never trained a model, or you want new algorithms and code.
Skills you'll gain
Machine learning strategyError analysisTrain/dev/test splitsBias and varianceML project decisions
Is this course right for you?
A good fit if you…
You already train ML models
You want better judgement on what to fix next
You are working through the Deep Learning Specialization
Consider something else if you…
You are new to machine learning
You want hands-on coding of new models
You want a long, comprehensive course
Requirements: Basic machine-learning experience; ideally the earlier Deep Learning Specialisation courses.
Realistic time: Around 5–8 hours.
About this course
Structuring Machine Learning Projects is the unusually practical third course of Andrew Ng's Deep Learning Specialization. It's not about new algorithms — it's about decision-making: how to set up train/dev/test sets, diagnose whether bias or variance is your problem, and decide what to work on next so you don't waste months optimising the wrong thing.
What you'll learn
Set up train/dev/test splits that reflect your real goal
Diagnose whether bias or variance is limiting your model
Decide what to improve next using error analysis
Use a single evaluation metric to guide iteration
Apply transfer learning and multi-task learning appropriately
Avoid common traps that waste weeks of ML effort
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
Free to audit on Coursera. A certificate needs a Coursera subscription (about $49/month) or Coursera Plus, with financial aid available. It is the third of five courses in the Deep Learning Specialisation.
Comparison · LBS
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A DeepLearning.AI certificate on Coursera is well respected as evidence of the learning, though it is a course certificate rather than a formal qualification.
Last updated
Instructor
AN
Andrew Ng
Coursera instructor
Andrew Ng — co-founder of Coursera and DeepLearning.AI — draws here on years of leading AI teams to teach the strategic instincts that separate productive ML work from spinning wheels. It's some of the most quietly valuable material in his specialization.