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Avis et commentaires pour d'étudiants pour Applied AI with DeepLearning par IBM

776 évaluations
130 avis

À propos du cours

>>> 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

Meilleurs avis


Apr 26, 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


Apr 17, 2020

Awesome!...Exciting!.Thanks for such an interesting hands on course..really appreciate all the tutors for all the valuable knowledge and helpful responses.

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101 - 125 sur 132 Avis pour Applied AI with DeepLearning

par Andrey O

Sep 07, 2018

Part with DeepLearning4J is not very good...

par Vinayak B

Jul 30, 2019

Really Helpful course for AI Enthusiasts

par Mobassir H

Apr 22, 2020

pytorch instructor was the best <3

par Valerio N

Mar 27, 2019

Very Complete course.

par Arati Y

Apr 09, 2018

It was nice

par Tobias H

Aug 26, 2018


par Pierre-Matthieu P

Nov 30, 2019

I was pretty disppointed overall.

Pros : we see many types of tools and get to use some of them in the programming assignments. I feel like I now have a general knowledge of the field. I particularly liked the aspects of scaling and deploying models in production.

Cons : This honestly feels more like a rough draft than a finished and polished course. I would have liked a consolidated overview of all these tools, their pros and cons, etc. Some tools and techniques were explained in literaly 15 min(!) and in some cases simply walked through a tutorial from the tool's website (!!). A programming assignment was broken through not being updated for more recent spark versions. Some videos mentioned a non-existent programming assignment (I assume they were reused from an internal IBM training session), etc. The comparison with say Andrew Ng's course on ML is cruel.

par Jakob S

Mar 26, 2020

The course covers some very interesting and important concepts, however on a very low level. The reason for this might simply be the lack of time; one cannot properly cover methods for AI image processing, NLP, etc. in such limited space. I also had mixed feelings about the exercises: It is very nice to see applications of the tools discussed in the lectures, but unfortunately the exercises are so simple that they can be easily finished without really understanding the code.

par Jose L M G

Apr 01, 2019

Lo hago, el curso es muy bueno en cuanto al uso de la plataforma watson, pero falla en explicar los fundamentos principales con animaciones, ejemplo, el curso de pytorch de udacity enseña eso muy bien. En lo demas esta bien, pero al no contar con elementos visuales de ayuda en laclase de LSTM se hace tediosa.

par Jeet D

May 12, 2018

The course is very resource heavy, i.e. it has great intuitive resources, but the learning experience was very poor. Some of the instructors were very sparse with the material contents, some just brushed over the contents without much explanation and.

The quality of the course has to be improved.

par Daniel P

Jul 10, 2018

Too much focus on IBM platform, good overview on Keras/SystemML/DL4J though, some presentations could have been better prepared and implemented. Overall an average Coursera course and not a particularly great experience to work through the material.

par Eugene N

May 22, 2020

Something happened to the free 1CPU 4GB python environment on IBM watson studio. It is unavailable and so I had to struggle with Skills Network Labs instead. Please can this be checked?

par 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.

par Mark B

Apr 17, 2020

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

par Raqui M

Apr 12, 2020

unfortunately the time series chapter is not complete

par Francesco d C

Nov 21, 2019

The lessons provided by Skymind were very poor.

par Csaba P O

Oct 01, 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.

par 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.

par 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.

par 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.

par Sheen D

Sep 01, 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...

par Quang A

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.

par Jorge A V

Feb 05, 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.

par Kaiwalya

Apr 02, 2020

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


Mar 24, 2020

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