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Learner Reviews & Feedback for Applied AI with DeepLearning by IBM Skills Network

1,085 ratings

About the Course

>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<< This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018 Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide. Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link

Top reviews


Oct 23, 2020

I learned many things from this course. However, I think in some points it could have been instructed much better. But all in all, it is a very worthy course for the price offered. Thanks a lot!


Apr 25, 2018

It was really great learning with coursera and I loved the course. The way faculty teaches here is just awesome as they are very much clear and helped a lot while learning this coursea

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151 - 175 of 188 Reviews for Applied AI with DeepLearning

By Mohsen F

Jan 3, 2021

it was not an advance course and course content and programming assignment is too easy. the whole course could bet finished in couple of days. video and presentation quality is poor.

By Nhu N

Sep 14, 2020

This course contain very good introduction to AI and DeepLearning. Although it assumed alot of knowledge already from the students in order to follow the content effectively.

By Robert F

Jul 4, 2020

Fairly okay course. Lectures were real hurried and high level. Had it not been for my Math and CS background I would not have gotten most of the material.

By Ceren A

May 10, 2020

Several lectures were superficial. I feel like I need to put a lot more time on my on to understand how to build a proper neural network model.

By Michael M

Aug 20, 2020

The programming assignments were easy. I wish it had some more difficult assignments requiring you to code your own neural networks

By Mario A C S

Sep 15, 2020

Good course, the explanation of CNN could be better, the anomaly detection exercise it's kind out of focus

By Arman I

Apr 14, 2021

Basic Deep learning; no programming assignment for most part of deep learning part


Jun 30, 2021

Os vídeo disponívei no youtube estavam tinha apenas a opção de legenda em alemão

By Mark B

Apr 17, 2020

Hard to follow ... found a lot of assistance in discussion forums

By Paul A

Nov 22, 2021

The concepts were rushed without concise explanations.

By Mehdi R

Apr 12, 2020

unfortunately the time series chapter is not complete

By Francesco d C

Nov 21, 2019

The lessons provided by Skymind were very poor.

By Jennifer K

Jul 4, 2021

Assignments are a bit too simple

By Csaba P O

Oct 1, 2019

I liked the general idea of this course, but the actual material is not as good as it could be. There are lots of inaccuracies in the material (like annoying typos and not working code examples) which should be corrected before you sell this course on Coursera.

I strongly suggest that you go through your material with someone who has pedagogy knowledge and who can assist you to improve the didactic aspects of your material.

I did this course (and the whole specialization) for the practical examples as I feel rather confident with the theoretical aspects of machine learning, but I wanted to learn how to do these things in Spark environment. At the end of the day I have got what I wanted (more or less, as the NLP part was really lousy), but if I would not have strong experience with the field, I would have been surely lost. Honestly, I would have a hard time to recommend these courses for someone who wants to learn about machine learning and not about how to do machine learning with Keras, etc. And I am sorry to say that, because, again, I liked the team, the attitude, and the technical aspects of this course.

By Eric C

Apr 29, 2020

There was a lot of interesting content, but I was sad that the programming assignments were fairly trivial. Any point where something deep and useful could have been assigned (I was hoping to get experience or guidance on building an LSTM autoencoder, for example) we were instead given a super easy alternative that was mostly pressing [Shift]+[Enter] on a Jupyter Notebook. SystemML seemed cool, but the only thing we ever did with it was multiply some matrices, and not even on a Spark cluster. I felt like I didn't learn that much because there was no point where I really had to engage my brain.

By luca t

May 17, 2020

Some lessons are too hard to follow because of stong foreign accents and poor grammar, so the student ends up spending most of their effotrs on tranlation. Instructors mostly go through coding notebooks quite quickly. The oil forcasting module is an example of the defects of the course: Poor language, poor presentation, 'here', 'there', 'now' are used to reference the code with no pointers, slides are borrowed from online sources and insufficient. The proposed code is a tentative time series forecating toy example with catastrophic results, not at par with any time series standard.

By Leonardo I

Aug 28, 2019

The course is delivered at a very high level of abstraction. If you are a beginner, I wouldn't recommend this course as the explanations provided are quite vague and not so good in many instances. Justifications for the use of quite a couple of algorithms/values are not provided thus leaving the learner with a lot of "Why's"

One of the nice things about the course is that the instructor responds promptly to students' queries.

By Sheen D

Sep 1, 2019

Again, the instructor speaks way too fast to explain anything. Even the subtitle cannot follow the instructor line by line. Frequent occurrence of inaudible words or sentence or wrong translations. When it comes to the code, never really understood what each line of codes is for...

By Hossein A

Jun 17, 2020

Very good topic with not very good instructors. The exercises and programming assignments are too way easy and do not help in getting enough knowledge. All instructors are not native English speakers and have very strong accent and speak slowly.

By Quang A D

May 25, 2020

It is difficult to fully understand the contents of the lesson, too many theories and not yet associated with practical problems. It's like studying in a university and for those who have more knowledge of math, not for everyone.

By Dr. J A V

Feb 5, 2019

Explanations are a bit rush. Would not be easy to follow if I would not have deep previous understanding on the Deeep learning topics.

By Kaiwalya

Apr 2, 2020

I felt the week 3 projects could have been given separate weeks to give better time for each project.

By John W

Aug 31, 2020

The progress through the material was good, but the delivery was quite boring.

By Mayank B

May 29, 2020

The assignments were not good enough to test what was taught.


Mar 24, 2020

No pedagogy. No instruction, mostly copy and paste and guess.