CODeepLearning.AI · on Coursera
Neural Networks and Deep Learning
Intermediate English Professional CertificateFREE
What you'll learn
Understand how a neural network makes a prediction (forward propagation)
Implement backpropagation and gradient descent from scratch
Choose and reason about activation functions
Build and train a deep network in Python and NumPy
Vectorise computations for efficiency
Develop the intuition to debug why a network isn't learning
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
Comparison · LBS
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Instructor
AN
Andrew Ng
Coursera instructor
— learners— courses — instructor rating
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.
Requirements
- Basic Python
- Comfort with high-school-level maths (matrices, derivatives help)
Who this course is for
- Aspiring ML engineers and data scientists
- Developers who want to understand deep learning, not just use it
- Students heading into AI research or applied roles
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
4.6 trust score
Frequently asked questions
Yes — the fundamentals it teaches (how networks learn) don't go out of date, which is why it's still the most-recommended starting point even as tools change.
Not strong, but some comfort with matrices and derivatives helps. Ng explains the maths gently as it comes up, so motivated beginners get through it.
You can audit it free on Coursera. The certificate and graded programming assignments require a subscription.
This is the first course of the Deep Learning Specialization, which goes deeper into neural nets specifically. The Machine Learning Specialization is broader and a gentler on-ramp — many learners do that first, then this.
You code. The assignments have you implement networks in Python and NumPy, which is exactly what makes the concepts stick.