Structuring Machine Learning Projects

Course 3 of 5 in the Deep Learning Specialization by Andrew Ng (DeepLearning.AI).

Topics

Course 3 of 5 in the Deep Learning Specialization by Andrew Ng (DeepLearning.AI).

Notes

Introduction to ML Strategy
deep-learning
ml-strategy
orthogonalization

Why machine learning strategy matters, and how orthogonalization gives you one knob per problem when tuning a supervised learning system.

Aug 7, 2026

Setting Up Your Goal
deep-learning
ml-strategy
evaluation-metrics
dev-test-sets

Single number evaluation metrics, satisficing versus optimizing metrics, choosing dev and test set distributions and sizes, and when to change your target.

Aug 7, 2026

Comparing to Human-Level Performance
deep-learning
ml-strategy
bias-variance
human-level-performance

Bayes optimal error, human-level performance as a proxy for it, avoidable bias versus variance, and the guidelines for improving your model.

Aug 8, 2026
 
Case Study: Bird Recognition in the City of Peacetopia
deep-learning
ml-strategy
case-study
flight-simulator

A machine learning flight simulator case study covering metric choice, dev and test splits, bias and variance diagnosis, and when to change the target.

Aug 9, 2026

Error Analysis
deep-learning
ml-strategy
error-analysis

Counting mistakes by category to decide what to work on next, deciding when incorrect labels are worth fixing, and why the first system should be quick.

Aug 9, 2026

Mismatched Training and Dev/Test Sets
deep-learning
ml-strategy
data-mismatch

Splitting data when training comes from a different distribution than dev and test, using a training-dev set to spot data mismatch, and how to address it.

Aug 10, 2026

Learning from Multiple Tasks
deep-learning
ml-strategy
transfer-learning

Transfer learning to reuse what a network learned on one task, multi-task learning to train one network on several tasks, and when each one makes sense.

Aug 10, 2026

End-to-End Deep Learning
deep-learning
ml-strategy
end-to-end

What end-to-end deep learning replaces, why some systems still work better split into stages, and how to decide whether an end-to-end approach fits your problem.

Aug 10, 2026
 
Case Study: Autonomous Driving
deep-learning
ml-strategy
case-study
flight-simulator

A machine learning flight simulator case study on autonomous driving covering error analysis, data mismatch, data synthesis, and transfer versus end-to-end learning.

Aug 10, 2026
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