The lectures were great and very easy to follow! However, I found that the assessments were too easy as they comprised solely of multiple choice questions, maybe including hands on coding assessments fo contribute to our final grade would have made me feel more confident that I've grasped what was supposed to be taught to me each week.

Modules need a bit of synchronization.

Please spend some more time explaining gradient descent.

If possible, explain using a board where we could have things simultaneously.

Also, request to have a course on machine vision using CNN etc.

This course is good for just theoretical understanding of the subject. But for practical implementation it is too hard to do.

Thank You team Coursera and Teachers from Duke University for helping me to understan dthe basics of machine learning..

More examples should be provided for each course, and the content needs to be more simplified.

The theory is well explained but you guys should update the coding parts to TensorFlow 2.

The course requires more detailed intuitive approach towards material preparation

some concepts were difficult to understand and not explained very well

I could not even understand the introduction class

Pretty good introduction to Machine Learning!

4.2

The concept is taught ok, but it's not great on hands-on learning.

T​opics are explained in a random and ilogical fashion. For example, they teach CNNs before gradient descent or the basics for training a model (like training splits or criteria to evaluate a model). Lack of order, toghether with confusing figures and poor metaphors, make impossible to properly understand any concept, and that can be seen in the discussion forums. In addition, there are gross conceptual errors, like suggesting that problems with non-linear solutions requiere NNs and can't be solved with linear regression algorithms. In fact, the whole course is centered in neural networks despite being presented as an introduction to ML and sells the idea that somehow NNs are the ideal solution to any non-trivial problem. This course is in shocking contrast with some other excelent courses that can be found in this pplatform, like the ones from Standford or Michigan.

muy poco ejemplos prácticos que se puedan seguir la momento de la explicación, hablan de una manera muy general pero no no dan ejemplos explícitos de su uso. no muestran como se transforma una palabra en un vector, el único ejemplo con el que se logra entender lo que esta realizando es el ejemplo inicial de los triángulos desplazándose sobre los filtros y las imágenes, en el resto de temática nunca aterrizan sus ejemplos

From my point of view as student who completed all weeks:

-The forum is totally dead. Nobody answers the questions since months. No support.

-You will not have a feedback of the assignments that you do.

-Confusing explanations. The order is not the correct

-Very theoretical and few practice.

You will need to search on internet other explanatioins to understand some parts. Hope standford course is better.

Very poor and low quality explanations.. lecturer starts the very first video mentioning about neural networks, convolutional networks, deep learning.. etc.. dropping a huge number of buzzwords and just following the script of the lecture, without realising, that he hasn't even said: WHAT EXACTLY Machine Learning is..

Very vague and obscure.

Not recommended.

Question 1 Quiz 1: Why is machine learning exciting? Really? Glad that's a free demo course, otherwise would've been a waste

Can't speed up the video. Speaker's too slow

Do not like how I cannot unenroll myself even if I see how this course is something I am not being able to manage.

I dont have the requird skills for the course and now I am unable to unenroll from the course.

Very advanced for an introductory course in my opinion

Please cancel this course. I want to opt out of it

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