Machine Learning Specialization vs Deep Learning Specialization

Machine Learning Specialization and Deep Learning Specialization are Andrew Ng’s two flagship courses on Coursera, and they build on each other rather than compete. Machine Learning Specialization is the foundational course — supervised and unsupervised learning, regression, classification and the core ML toolkit in Python. Deep Learning Specialization goes deeper into neural networks — CNNs, sequence models and TensorFlow. Most learners do Machine Learning first, then Deep Learning.

Side-by-side comparison

Price, certificate, length, rating and more — straight from each course, compared field by field.

Course	Machine Learning Specialization	Deep Learning Specialization
Platform	Coursera	Coursera
Offered by	DeepLearning.AI	DeepLearning.AI
Topic	AI & ML	AI & ML
Level	Intermediate	Intermediate
Skills taught	Machine learningSupervised learningUnsupervised learningRegressionClassificationPythonscikit-learn	Deep learningNeural networksConvolutional networksSequence modelsTensorFlowHyperparameter tuning
Length	86h	130h
Price	Audit free · Cert $49/mo	Free to audit, paid certificate
Certificate	Specialization	Specialization
Certificate cost	Free to audit · paid certificate	Free to audit · paid certificate
Rating	4.9 (39,000)	4.8 (147,000)
Enrolled	820K	995K
Language	English	English

Figures are pulled from each course listing and can change — always confirm price, certificate cost and availability on the provider’s own site before enrolling. We’re not affiliated with either provider, and blank fields mean the data isn’t published, not zero. Last reviewed August 2026.

Which should you choose?

There’s no single winner — it depends on where you’re starting and where you’re headed. If you are…

You’re new to machine learning → Machine Learning Specialization

You want to focus on deep learning and neural networks → Deep Learning Specialization

You want the broadest ML foundation → Machine Learning Specialization

You want computer vision, NLP or sequence models → Deep Learning Specialization

You want the shorter option → Machine Learning Specialization

You want hands-on TensorFlow → Deep Learning Specialization

You’re not sure which to do first → Machine Learning Specialization

Machine Learning Specialization is the right starting point and covers broad ML foundations, while Deep Learning Specialization goes deeper into neural networks — most learners do Machine Learning first, then Deep Learning.

Best for: Both are taught by Andrew Ng and suit anyone serious about learning machine learning properly. They’re a sequence more than a choice: Machine Learning Specialization builds the foundations — the algorithms, intuition and Python workflow — and Deep Learning Specialization builds on that with neural networks and modern architectures. You get guided teaching, hands-on labs and a genuine understanding of the maths behind the models.

Career outcomes: Both build toward roles like Machine Learning Engineer, Data Scientist and AI Engineer. The Machine Learning Specialization gives you the foundations most ML roles assume; the Deep Learning Specialization adds the depth needed for computer-vision and NLP work. Neither is a job on its own — what gets you hired is applying them in real projects and a portfolio. Comfort with Python and some basic maths helps before you start either.

Frequently asked questions

Yes — do Machine Learning first. It’s the foundation, and the Deep Learning Specialization assumes you already understand core ML like regression, classification and gradient descent.

Yes. It goes deeper into neural networks and the underlying maths and is longer. Doing the Machine Learning Specialization first makes it far more approachable.

Some comfort with Python and basic linear algebra and calculus helps for both, and matters more for Deep Learning. You don’t need to be an expert, but complete beginners should shore those up first.

You can, but most people struggle without the ML foundations. Unless you already know core machine learning, start with the Machine Learning Specialization.

It depends on the role: broad ML roles value the foundations, while computer-vision and NLP work needs the deep-learning depth. Many candidates do both, plus a portfolio.

The Machine Learning Specialization is around 90 hours (about two months at ten hours a week); the Deep Learning Specialization is longer at roughly 120 hours. Both are self-paced.