Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
DeepLearning.AI · View on Coursera ↗
Taught by Laurence Moroney
Data ScienceComputer Science
About the course
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the DeepLearning.AI TensorFlow Developer Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning.
The dissent
The most-upvoted critical review (113 votes, 2.0★).
“This felt like a glorified tutorial for TensorFlow/Keras. I expected more in-depth treatment of the material. E.g. covering more ground (regularization wasn't mentioned at all), or going into more depth on the machine learning theory (why are we using this activation function, this loss, or this optimiser) or practical tips (e.g. discussions of network design) or the tools we are using (e.g. what exactly is TensorFlow, what is Keras, how do they relate to each other, how do they work under the hood).”
Coursera's 4.8 comes from 19,749 ratings. Of those, 1.2% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 23 Oct 2026.Read them on Coursera ↗