Much weaker than Stanford offerings. Strange buildup of topics for a breezy, but not particular accurate understanding. For example: multiple layers of a neural network is introduced before multiple category classification. Transfer learning is introduced incorrectly. The matrix representation of multiple features of an example with multiple examples is introduced very late in the course. The instructor is conscientious and seemingly knows the material despite using non-standard terminology. One wonders if he is primarily a teacher/researcher and rarely a practitioner. One wonders if Duke is a leader in machine learning research.

Very good introductory course ,very well designed and professors explaination is very easy to understand .Go for it guys !

Happy learning !!!!

Sonic Somanna PK

This was a really great course for understanding the basics of machine learning through a lot of simple but relevant, real world examples.

Although the course is great from a theoretical point of view, but it has two major flaws. First, it doesn't provide the fundamentals of Machine Learning but instead directly moves to Deep Learning, although building those concepts from ground up. Also, from a practical point of view, this course is really lacking as there is not a single explanation video on any of the coding aspect of Deep Learning and the videos that even exist just ask us to read through the Documentation to learn the practical aspect.

Excellent course. Concepts such as gradient descent and convolutions as they pertain to neural networks are explained without going into the mathematical details but, in my opinion, are explained more intuitively and better, as compared to most other courses. The course does include some ungraded Jupyter notebooks exemplifying key elements of deep learning networks. Highly recommended to 'cement' understanding of neural networks.

Great course ! Pr Carin is clear enough to make you understand complex concepts like LSTM. The Math, calculus, algenra and prob are not too difficult. I enjoyed to follow this course ! To conclude a good introduction to ML to make you go deeper into the subject

It was great to touch new professional area and to understand its fundamentals. The course gives a broad view on machine learning, so I think now I really understand, what the machine learning is and how to use it in my work and even my political investigations.

A very concise and yet beautifully constructed course for introduction to machine learning for absolute beginner having basic knowledge of probability and mathematics.

I thought this was a great course to build up an intuitive understanding of a few different machine learning techniques. It is certainly skewed more towards breadth than depth, but this is unavoidable given the short length of the course.

The concepts presented are very clear. I understand a little more about machine learning thanks to the course. The support of the concepts using PyTorch was also an interesting aspect in terms of integrating theory and practice.

It's really an amazing field to learn new things and from institute is like Amazing to me I've learnt more ...it's not at all boring and we'll will be excited for future experience with you 💯

Very good introductory course, I highly recommend it to anyone looking to get a flavour of the methods behind the recent advances in AI without going into super-technical details.

This course give a good introduction toward machine learning and AI. someone who wants to pursue his/her career in ML and AI in future this course would definitely help him/her

I liked the pace and the tensor flow applications. This should be upgraded to TF 2.0 at some point. Also, I would've appreciated some GAN material.

I like this introductory course, very good one to start to learn machine learning. I will definitely continue studying and re-watch the videos.

Very Good course explaining the theoretical concepts related to deep learning . Thank you

A very fine tuned Course,used as a warm up course for deep learning,highly recommended

Great course for beginners, did a lot to fill in the gaps in my knowledge. There could be a little more help with the actual coding parts of the project, the work done in ipython notebook is largely self-taught.

The course is a good balance between learning key concepts and doing coding, the coding being optional. The phrasing of quiz questions and answers were sometimes confusing.

GOOD COURSE

I was an engineering major at Duke, but never took any sort of computer science/machine learning classes because I didn't have time. This class was super straight forward. Everything just made sense. I don't know how to say it other than that. It was great to see how much of the math and signal processing things I learned could be applied to something like machine learning. Before this class, I had no clue what machine learning was, and now I feel like I understand the main gist and the basis for all of the math behind it.

Very nice introduction to machine learning with great exemples and teachers. Each lab time (1h each) was overly underestimated in my case for a newbie, 1h would translate into half a day or a full day. And I think a lot more could be explained about PyTorch coding exemples given in the labs, like the choices for filter size dimensions, but overall it was doable.

Really well explained! It was very interesting to learn and as it was from the start it was easy to understand as well. Just one suggestion for the programming assignments also provide the solutions and explain how the coding have been done as understanding that is a bit difficult. Else it was a great course and i really loved it.

Really enjoyed the course - it was a bit heavy and required getting used to (also this was my first time doing a course online) but the course has a nice rhythm and it was excellent once i got the hang of it. Very useful course to get an insight into the world of Machine Learning and to increase curiosity and interest.

Extremely well structured course. The instructors are knowledgeable and their teaching method is easily interpretable. The course mainly focuses on ML algorithms and how they function , without going too deep into the mathematics , although some mathematics knowledge is surely required before taking this course.