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Opiniones y comentarios de aprendices correspondientes a Applied AI with DeepLearning por parte de IBM

1,037 calificaciones
179 reseña

Acerca del Curso

>>> 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

Principales reseñas

23 de oct. de 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!

25 de abr. de 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 de 181 revisiones para Applied AI with DeepLearning

por Mario A C S

15 de sep. de 2020

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

por Arman I

14 de abr. de 2021

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

por ENY D F G

30 de jun. de 2021

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

por Mark B

17 de abr. de 2020

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

por Paul A

22 de nov. de 2021

The concepts were rushed without concise explanations.

por Raqui M

12 de abr. de 2020

unfortunately the time series chapter is not complete

por Francesco d C

21 de nov. de 2019

The lessons provided by Skymind were very poor.

por Jennifer K

4 de jul. de 2021

Assignments are a bit too simple

por Csaba P O

1 de oct. de 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.

por Eric C

29 de abr. de 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.

por luca t

17 de may. de 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.

por Leonardo I

28 de ago. de 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.

por Sheen D

1 de sep. de 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...

por Hossein A

17 de jun. de 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.

por Quang A D

25 de may. de 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.

por Jorge A V

5 de feb. de 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.

por Kaiwalya

2 de abr. de 2020

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

por John W

31 de ago. de 2020

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

por Mayank B

29 de may. de 2020

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


24 de mar. de 2020

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

por Kleuber R A

15 de jul. de 2021

Disappointing! The IBM name associated with the course raises a very high expectation. However, right from the start it is clear that it was done in a hurry with many typos, bad videos and recorded inside the car (amateur thing), the didactics of some instructors is terrible to be understood and translated into other languages. I believe the IBM name is associated with the courses to bring more credibility, but it's disappointing in the end. The material is very interesting, but the way it was delivered in this course leaves a lot to be desired.

por Estevão L

10 de jun. de 2020

Terrible course, it looks like it was made in a hurry (this whole IBM specialization does): a LOT of typos, videos recorded with many mistakes, A LOT of very, very ugly and nonsensical code, bad didatics... honestly, it looks like someone planned this whole entire thing during lunch and then the instructors just recorded the videos in their spare time, if anything, this is IBM marketing (and bad marketing).

Don't bother with this course, there are many other good courses out there and this one is not one of them.

por Felipe M

18 de sep. de 2019

Videos are old. It feels like he had a bunch of material and put them together to create this course. For example: There are assignments that they give you the answer because the questions are not supposed to be there. He doesnt teach, instead, he reads a script. The assignments are not challenging and you dont feel like you learned. Horrible and painful.

por Nikat M

29 de may. de 2020

course material and tutorial not in sync, assignment and software not in sync, no support or help available, poor or no explanation of problem that is being solved, the instructors maybe technical but do not display understanding of business solution, poor story boarding approach

por mathias s

16 de mar. de 2018

The instructions are missing a lot of things and repeating them self with some modifications, that changes some things that you have to do.

There is also general problems with the IBM setup, when using the services, due to missing selection setup.