学生对 杜克大学 提供的 Introduction to Machine Learning 的评价和反馈

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.

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.

The course is okay, the teaching is helpful explaining the concepts of machine learning well. The problem is the connections between theory and practice. The assignments in pytorch are completely decoupled from the course materials and not explained very well. They expect you to code a model directly from mathematical theory with poor explanation of the pytorch framework and syntax.

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.

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.

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

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.

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.

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.

I felt that I took the best descition in taking this course, because the professors took this course with atmost clarity and made even the difficult concepts understand easily.

Thank you Professors

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.

I have a background in pathology and I wanted to understand how machine learning works so that I can take an active part in the changes within my field and understand what is happening. This course was an amazing experience of learning, for someone like me with no background in calculus or linear algebra.

A beginner like me who wanted to learn and expirement with machine learning but didn't know where to start, well this is the best course for you. The learning process is highly engaging and the concepts are explained in a well-refined manner.

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.

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

I would like to thank Prof. Carin for a very lucid and intuitive explanation of the major concepts in Machine Learning covered in this class. This is the best explanation of the concepts of CNN and Reinforcement Learning that I have found so far !!!

I am also a little bit disappointed by the set of Programming Exercises at the end of some the lectures by other teachers. I think instead of giving students examples of programming using raw, low-level TensorFlow APIs because it overwhelms the main concepts. It is better to use high-level back end tool like Keras (NOT Slim !!!)

It is a very basic introductory course to important fields in machine learning. It tells important models like CNN and RNN and LSTM. but it does not go deeper into the technical levels of these models. Some parts about mathematics are not very satisfying. Also I feel like the course doesn't provide enough training for the coding work. Nonetheless, it is a good course to start with machine learning and the instructors repeat the concepts from the previous class, which helps me a lot in understanding the concepts.

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.