Introduction to Deep Learning & Neural Networks with Keras
by IBM Skills Network · Coursera
Our Verdict
Worth it — with caveatsTake this as a fast, friendly on-ramp to deep learning, not as a deep technical training: the official IBM/Coursera page rates it 4.7/5 across 2,121 reviews, with 75.5% of learners giving 5 stars. Taught by Alex Aklson of the IBM Skills Network, the roughly 10-hour course explains neurons, forward/backpropagation, gradient descent, the vanishing-gradient problem, activation functions, and how to build regression and classification models in Keras, plus a high-level tour of CNNs, RNNs, transformers and autoencoders. Its strength is conceptual clarity and how little code Keras requires; its recurring real weakness, surfaced repeatedly in critical reviews, is that hands-on coverage is shallow and the final peer-graded project expects Keras routines not actually demonstrated in the lectures. Coursera labels it Intermediate (the catalog's 'beginner' tag understates the math expectation), and it now serves as Course 1 of IBM's 'Deep Learning with PyTorch, Keras and Tensorflow' Professional Certificate. Good first step for the concepts; pair it with a more code-heavy course if you want production deep-learning skill.
Highly rated (4.7/5, 2,121 reviews) and genuinely clear on fundamentals and the Keras workflow, but it is a high-level overview rather than a rigorous build-it-yourself course. Multiple verified reviews note shallow practical depth and a final project that uses routines never shown in the lectures, so it is worth taking only if you want the concepts first and plan to follow up with deeper, code-focused material.
Best for: Learners who already know Python and basic ML and want a clear, low-friction conceptual introduction to neural networks and the Keras API before going deeper. A strong fit for people enrolling in the broader IBM 'Deep Learning with PyTorch, Keras and Tensorflow' Professional Certificate (where this is Course 1), or anyone who wants to understand backpropagation, gradient descent and CNN/RNN/transformer concepts at a high level and earn a shareable IBM-branded certificate.
Skip if: Complete beginners with no programming or linear-algebra/calculus exposure (Coursera labels it Intermediate), and engineers who want rigorous, hands-on implementation of CNNs, RNNs, dropout and pooling in Keras. Reviewers explicitly flag that these are discussed conceptually but barely coded, and the final assignment expects routines not taught. People who want PyTorch, current LLM/GenAI depth, or research-grade math should choose something heavier.
About This Course
Build and train neural networks using Keras covering forward propagation, backpropagation, and convolutional networks.
What You'll Learn
Curriculum
~1 hour. Neural network architecture and operation; forward propagation.
~2 hours. Gradient descent, backpropagation, the vanishing-gradient problem and activation functions.
~2 hours. Building regression and classification models with the Keras library.
~3 hours. Shallow vs. deep networks, plus CNNs, RNNs, transformers, autoencoders and pretrained models (conceptual).
~2 hours. Peer-graded project (image classification / caption generation) and course summary.
Prerequisites
- Working knowledge of Python
- Basic machine learning concepts (regression, classification)
- Comfort with introductory linear algebra and calculus (Coursera lists the course as Intermediate)
Instructor
IBM Skills Network
Instructor · Coursera
Pros & Cons
Pros
- Concepts explained clearly and accessibly; reviewers repeatedly call it well-structured with a knowledgeable instructor (Alex Aklson, IBM Skills Network)
- Demonstrates how little code Keras needs, making the model-building workflow feel approachable
- Strong, consistent ratings: 4.7/5 from 2,121 reviews with 75.5% five-star on the official page
- Free to audit; only the shareable IBM-branded certificate requires a paid Coursera subscription or Coursera Plus
- Slots cleanly as Course 1 of IBM's 'Deep Learning with PyTorch, Keras and Tensorflow' Professional Certificate for learners wanting a structured path
Cons
- Shallow hands-on depth: multiple reviews note key topics (dropout, max pooling, CNN, RNN, padding) are discussed but barely implemented in Keras
- The final peer-graded project expects Keras routines that several reviewers say were not demonstrated in the lectures
- Some course materials use a synthetic/robotic narration voice (flagged in the E-Student review of the IBM AI Engineering track)
- Catalog's 'beginner' label is optimistic; Coursera classifies it Intermediate and expects prior Python/ML exposure
Alternatives To Consider
Frequently Asked Questions
Is Introduction to Deep Learning & Neural Networks with Keras free?
Introduction to Deep Learning & Neural Networks with Keras is $49/mo. Free to audit the course materials; the shareable certificate requires a paid Coursera subscription (catalog lists ~$49/mo) or Coursera Plus. Financial aid is available. As Course 1 of the IBM 'Deep Learning with PyTorch, Keras and Tensorflow' Professional Certificate, one subscription covers all 5 courses.
Who is Introduction to Deep Learning & Neural Networks with Keras for?
Learners who already know Python and basic ML and want a clear, low-friction conceptual introduction to neural networks and the Keras API before going deeper. A strong fit for people enrolling in the broader IBM 'Deep Learning with PyTorch, Keras and Tensorflow' Professional Certificate (where this is Course 1), or anyone who wants to understand backpropagation, gradient descent and CNN/RNN/transformer concepts at a high level and earn a shareable IBM-branded certificate.
What will you learn in Introduction to Deep Learning & Neural Networks with Keras?
Describe foundational deep learning concepts: neurons, artificial neural networks and how forward propagation works; Explain training challenges including gradient descent, backpropagation, the vanishing-gradient problem and activation functions; Build regression and classification models with Keras and evaluate their performance; Differentiate shallow vs. deep networks and the role of supervised vs. unsupervised deep learning.
What are the prerequisites for Introduction to Deep Learning & Neural Networks with Keras?
Working knowledge of Python; Basic machine learning concepts (regression, classification); Comfort with introductory linear algebra and calculus (Coursera lists the course as Intermediate).
Is Introduction to Deep Learning & Neural Networks with Keras worth it?
Highly rated (4.7/5, 2,121 reviews) and genuinely clear on fundamentals and the Keras workflow, but it is a high-level overview rather than a rigorous build-it-yourself course. Multiple verified reviews note shallow practical depth and a final project that uses routines never shown in the lectures, so it is worth taking only if you want the concepts first and plan to follow up with deeper, code-focused material.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Coursera's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Coursera - official course page (rating, syllabus, level, price)
- Coursera - learner reviews page (4.7/5, distribution, verbatim reviews)
- E-Student - review of IBM AI Engineering Professional Certificate (strengths/weaknesses incl. this course)
- Coursera - IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate (parent program)
- Class Central - course listing (provider/instructor cross-check)