Grades that depends on peer aasigments when there is no activity in the forums is not a good idea and a bad image for the institution (IBM)

good course, PROS: very well presented, clear amd methodic. appropriate tasks. CON the name suggests that RL would be a significant topic, while in reality it appeared only in the end of the course and important subjects such as TDlearning are missing.

the content is presented in a bullet manner without any deep dive into any algorithm. You can get a good overview of various models in DL and RL, but nothing in details. I would not recommend any DS to use this course as a learning module. But maybe it is quite suitable for people without data science background. Even though, the presentation is not interesting. Just read-out of bullet points for each model.

Reinforcement Learning part needs to be a separate course and more details in it

Thank you Coursera.

Thank you IBM

Thank you to all instructors

this course is awesome

Well balanced course

Nice initiative!!!

Excellent course

Well prepared, gives a good intro to multiple Deep Learning algorithms and good examples to cover the major topics. A few more practice labs on CNN and RNN would have been awesome!

Cons : The only difficulty I found was with the english accent of our dear trainer. Sometimes it was really very difficult to comprehend what was being said and one needed to rewind the video multiple times and read the subtitles. Other than that, nothing to complain.

Cheers!

there is no significant detail about RL

This course has a larger scope than the other ML certificate courses and is a little out of date. While it introduces RL, it does not discuss TD learning or Deep RL. RL seems "tacked on". Similarly, there is a brief introduction to Attention, but no substantial discussion of Transformer models (I suggest dropping LSTM and talking just about Transformers). Unlike the other courses, which introduced the concepts and also covered practical steps on using these methods, the DL/RL course is a little light on the practical side of DL. There is little discussion of why particular architectures are chosen for specific problems or how sensitive those architectures are to various hyperparameters. You will know what DL, CNN, RNN (and to a lesser extent, RL) are is when you finish this course, but there's a big gap for any practical use of these tools, which was less of an issue for the (admittedly simpler/more scoped) topics in earlier courses.

please fire whoever created the slides. the presentation is very lazy. and the content is rushed

The instructor just read the unclear slides

The jupyterlab does not work

The IBM Machine Learning Professional Certificate course is one of the complete course for someone familiar with python and wanting to learn different machine learning techniques. The second last course of this professional certificate Deep Learning and Reinforcement Learning is a good courses which tries to introduces Neural-Net, CNN, LSTM, Reinforcement Learning and other deep learning concepts. As deeplearning is a vast subject and there are several specialization available in Coursera. This single course provides a good introduction of the subject matter.

I highly recommend this specialization for anyone who is aspiring to become a data-scientist / Machine learning expert.

This course felt rather different to the previous courses that involved classical machine learning. While the previous courses were a deep dive into some statistical techniques with an intermediate complexity, this course felt like a surface level introduction to a very wide and deep topic - despite the fact that this is the longest course in the specialisation. I don't think this is a flaw in the course, but it probably means that I need to look into more courses on deep learning in order to gain a fuller understanding of the topic. This was a very interesting and useful introduction to the topic though!

The course is excellent. Eather you want to learn something new or refresh your knowledge - go for it. The only minor thing is that other peers are grading your final project (but this is just a small portion of a final grade). Some participants can submit their reports written not in the English language. Some supervision of the final project would be helpful.

Excellent Course with step by step instructions. Great for a neuro diverse person like me. Thank you course developers and the team for such a simple to follow logical course.

The concepts were clearly explained in lectures. The assignments were very helpful to gain a practical insight of the skills learned in the course.

Extraordinary course, one of the best in coursera!, Reinforcement Learning and Autoencoders can have better examples.

Complex concepts and techniques introduced in a very simple and comprehensive manner. Perfect intro to deep learning

Excellent from the theory and the practice! Great explainatory videos and detailed jupyter notebooks!

Hello, thank you again for the course. My congrats, once more, to the instructor on the videos!

The difficult terms are simplified enough for understanding and application in real life.