22 de nov de 2017
I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
1 de dez de 2020
I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
por Juan Z•
9 de nov de 2019
This course is less pratical and theoretical. I don't mean it is not helpful to me. I think this course might be helpful as guideline when I hand on the real project in future
por elie a•
4 de nov de 2019
very good course, but I felt like it was lacking one more week of course to get deeper knowledge about how to really get data sets and how to set them up for real applications.
por Gabriel O•
1 de mai de 2022
While the course teach valuable concepts, the material (especially the quizzes) contained many problems, ranging from grammar mistakes, to ambiguous writing, to wrong answers.
por Christian V•
18 de jul de 2019
you may think because the course is shorter will be much easier but the videos has a lot of information to process. I am excited to tried this techniques on real applications!
por Ambrose S O O•
25 de mai de 2019
A good course. Provided general high level thinking and reasoning for quick problem solving, data management, multi-tasking, transfer learning, and error reduction techniques.
por Sayantan A•
22 de mai de 2018
Not as exciting as the previous courses, but informative nonetheless. A section for handling imbalanced or skewed datasets would be useful, especially for multi-task learning.
por Aleksi S•
22 de fev de 2018
Not as deep into details as the two first courses in the specialisation, but nevertheless I learnt a lot of techniques that I hope will be feasible when I work on AI projects.
por Charles S•
28 de nov de 2017
Excellent lectures and notes as always. Great insights and clearly explained. I think we could have used a programming exercise on transfer learning at least in this section.
por Akanksha D•
7 de jan de 2018
More coding exercises could be included with much more mathematics background explained. Videos could be made a little shorter. There is redundancy in the some of the videos.
por Juan M•
4 de jan de 2018
Good overview of how to structure ML projects with great practical advice. I wish the course had includes a programming lab to help us try out and practice some of the ideas
por Aravindh V•
29 de ago de 2020
Good content. The tips and tricks a experienced AI practitioner has was shared. But at least one programing exercise applying all the concepts learnt, would have been great.
por Luis J P M•
12 de jan de 2020
In the first quiz, the comments about why an answer is correct are too simple. On the contrary, in the second quiz, the comments are really good and give us better feedback.
por Uddhav D•
2 de jun de 2019
I feel more Examples should be given regarding the variable and bais tuning, also Error analysis videos should be a bit in-depth. Everything else is as good as it can get :)
por Jean M A S•
27 de out de 2017
The simulations were very good to build a good intuition about setting up a machine learning project.
But I regret that we didn't have coding exercises. 4 stars for this one.
por Carlos S C V•
15 de abr de 2020
Me gustó el curso, pero creo que algunas lecciones fueron un poco más largas de lo necesario. Debo agregar que me gustaron mucho los simuladores, creo que me ayudaron mucho
por Vinod S•
19 de nov de 2017
Helps clearly in understanding practical aspects of deep learning. An additional week, highlighting the aspects of productionizing a deep learning project would have helped
por Vinay N•
12 de jul de 2020
Since I myself am working on a few projects, the concepts here are somewhat useful in error reduction. Especially when the models are used to automate medical applications
por Palathingal F•
28 de set de 2017
A unique course to understand the process of establishing a ML project. But lacks tools information and a more structured definition of the process. A bit too theoretical.
por Mahnaz A K•
2 de jul de 2019
Thanks for the practical tips and insights from real projects.
Your pool of heroes of deep learning is very skewed. If the field is so skewed, then it's a bigger problem.
por Vivek V A•
13 de fev de 2019
Good course for the ones who already started developing ML systems. This will help us in improving the ML systems and identify what can be done for which kind of problems
por Ivan L•
25 de jun de 2019
Most of the material was quite useful, but some was, perhaps, too obvious. Also, some things were discussed too thoroughly, and, in my opinion, that was a waste of time.
por Алексей А•
14 de set de 2017
Would be great to obtain more concrete information.
For example, instead of "requires much more training data" to obtain "requires ~1'000'000 samples instead of ~100'000"
por Rafal S•
22 de jul de 2019
Excellent content overall. However, reiterates some of the knowledge already presented in the two previous courses of the specialization. Lacks programming assignments.
por Amir R K P•
7 de dez de 2018
I wish there was more examples, visualization and depiction of work with referral to papers or experiment here. or perhaps a bit of project management, data management.
por Pete C•
24 de jun de 2018
Enjoyable, but felt a little less challenging and more hastily assembled. Regardless, the material is valuable and as always, a pleasure to be instructed by Andrew Ng.