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DeepLearning.AI · on Coursera

Neural Networks and Deep Learning

Intermediate English SpecializationFREE
SkillsNeural networksDeep learningPythonBackpropagationGradient descentNumPy

Is this course right for you?

Our take
Andrew Ng's opening course in the Deep Learning Specialization — for many people, where neural networks finally click.

Good for: Genuinely understanding how neural networks work, from a renowned teacher.

Skip if: You want a no-maths overview or a plug-and-play library tutorial.

It earns that reputation by refusing shortcuts: instead of handing you a library, it walks through the mechanics — forward and back propagation — and has you build a network step by step in Python. That's exactly why it's the wrong first stop if you wanted a no-maths overview or a plug-and-play tutorial; it expects some comfort with maths and code and rewards it with real understanding.

It's the first of five courses in the specialisation, free to audit on Coursera, with the DeepLearning.AI certificate available on a subscription or Coursera Plus (financial aid available) — a credential the ML world takes seriously as a marker of the work done. The core ideas here are foundational and don't age (as of 2026).

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About this course

Neural Networks and Deep Learning is the first course in Andrew Ng's celebrated Deep Learning Specialization, and it's where most people's real understanding of neural nets begins. Rather than hand you a library, it walks you through the mechanics — forward and backward propagation, activation functions, gradient descent — until you can implement a network in Python yourself.

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Instructor

AN
Andrew Ng
Coursera instructor

Andrew Ng — co-founder of Coursera and DeepLearning.AI, founder of Google Brain — is widely regarded as the most effective teacher in the field. His deep-learning courses are the ones practitioners most often name as the foundation of their understanding.

Frequently asked questions

You need reasonable comfort, not a maths degree. It helps to know basic linear algebra and calculus concepts, since neural networks rest on them, but Andrew Ng explains the maths intuitively as it comes up rather than assuming deep prior study. If your maths is rusty, you can follow it with effort; if it is entirely absent, a quick refresher on the basics first makes it much smoother.

Yes. It is the first course of the Deep Learning specialization and involves programming assignments in Python, so you should be comfortable with the language and ideally some NumPy. It is not a Python primer. You do not need prior deep-learning knowledge, which it builds from scratch, but arriving without working Python means struggling with the code rather than the concepts.

No — it is course one of five in Andrew Ng's Deep Learning specialization. It covers the foundations: what neural networks are and how to build and train them from the ground up. The later courses go into improving networks, structuring projects, convolutional networks, and sequence models. You can take this alone for the fundamentals, but the full picture spans all five.

The fundamentals it teaches — how neural networks work and are trained — are foundational and do not go out of date, so it remains genuinely valuable. What it does not cover is the very latest architectures and tooling, since the field has moved on since it was made. Treat it as the solid grounding it is, then learn current developments (transformers, modern frameworks) on top.

Yes, you can audit it on Coursera and watch the lectures at no cost. The graded programming assignments — where much of the learning happens — and the certificate require a subscription, with financial aid available. Since building the networks yourself is central here, the paid track is worth considering if you want that hands-on practice.
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