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DeepLearning.AI · on Coursera

Deep Learning Specialization

4.8(147,000) on Coursera·995K enrolled
Intermediate 130 hours English Specialization
SkillsDeep learningNeural networksConvolutional networksSequence modelsTensorFlowHyperparameter tuning

Is this course right for you?

Our take
The standard path into neural networks once you have the basics, and it is free to audit. It is more advanced than Ng's Machine Learning Specialization, so take that one first.

Good for: learners who know some Python and machine-learning basics and want to go deep on neural networks.

Skip if: you are new to machine learning, or you want a quick overview rather than five courses.

Across five courses you implement networks from scratch in NumPy to understand the maths, then scale them with TensorFlow, covering CNNs for vision and sequence models for NLP. The often-overlooked third course on structuring ML projects teaches the diagnostic decisions experienced practitioners make and most courses skip, which is a large part of the value.

It is not a quick overview and not for someone new to machine learning — it is five substantial courses that assume Python and ML foundations. The certificate needs a paid subscription; the free audit covers the learning. Some frameworks have moved on since filming, but the fundamentals hold (as of 2026).

Comparison · LBS

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About this course

The Deep Learning Specialization is the natural follow-on to Andrew Ng's Machine Learning Specialization and goes deep into neural network design and training: implementing networks from scratch in NumPy to understand the math, then applying them at scale with TensorFlow. Five courses cover neural network foundations, improving networks (hyperparameter tuning, regularization, optimization), structuring ML projects (the practical decision-making most courses skip), convolutional networks for vision, and sequence models for NLP and audio.

Machine Learning vs Deep Learning Specialization (2026)
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Instructor

AN
Andrew Ng
Coursera instructor
1.3M+ learners12 courses4.9 instructor rating

Taught by Andrew Ng, Co-founder of Coursera and DeepLearning.AI, former Head of AI at Baidu and Google Brain, the most-trusted ML educator of the last decade.

Frequently asked questions

For most people, yes. This one is more advanced and assumes you already grasp the basics of machine learning; Andrew Ng's Machine Learning Specialization is the gentler on-ramp. If you already know ML fundamentals and some Python, you can start here directly — but jumping in cold tends to be a struggle.

Less than people fear. You need comfort with matrices and the idea of a derivative, but Ng deliberately teaches the calculus and linear algebra as they come up, keeping it accessible. You won't be deriving proofs; you'll be building an intuition for how the maths drives the networks.

TensorFlow, with Keras. That's worth knowing, because PyTorch has become the default in research and much of industry. The good news is the concepts transfer — once you understand the ideas here, picking up PyTorch takes weeks, not months. If you're aiming at machine-learning engineering roles, plan to learn PyTorch as well.

The foundations it teaches — neural networks, CNNs, RNNs, attention — are timeless and still exactly what you need. But it predates the generative-AI wave, so it doesn't cover fine-tuning large language models, retrieval systems or prompting. Treat it as the bedrock, and add a dedicated generative-AI course for the 2026 skill set.

Different philosophies. This specialization is theory-first and well-structured — you build understanding of why things work from the ground up. fast.ai is code-first and practical, getting you to results quickly with less maths, using PyTorch. Many people do this for the depth and fast.ai for the hands-on speed; they complement each other well.

It's a respected foundation and genuine coding practice, but not a complete job path on its own. Employers want to see projects you've built, and increasingly PyTorch and generative-AI skills too. Use the specialization to understand the field properly, then build your own work on top — that portfolio is what actually gets interviews.

Yes — each of the five is a standalone course, so if you specifically want the one on sequence models and attention, you can take it alone. That said, they're designed to build on each other in order, so unless you already have the earlier material, the full sequence makes more sense.
Paid
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