This is a very good introduction to machine learning topics. From history to implementation of the concepts in different fields of machine learning, it helps developing a good sense about machine learning. As somebody with no knowledge before this course, it helped me a lot to understand machine learning and the areas that I possibly like the most.

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.

Just learning the course and loved it coz of its depth in knowledge in Machine Learning.

Week 1 covers almost all fundamentals topics and lays the solid foundation to the learner in ML.

Looking forward to enjoy learning this course attentively, complete the assignments.

I suggest to every who is interesting in online course , should study with coursera.org.

They will teach you in a such that all your doubts will be cleared.

They have quiz and assignment at the end of lecture.

So I strongly recommend this to every one.

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.

I simply loved the course. I've been working with Machine Learning, but I didn't understand much about Deep Learning - this course helped me a lot to get started in this new research area.

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 instructional with lucid explanations, the hands-on practical or lab sessions helped me to actually practice what I have learnt.

It is a good and awesome course because it takes you by hand from the basics of ML to the most complex models.

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

It was very good , understood all concepts and learned something new.

Labs are great hands-on training, but the lectures and lab texts don't sufficiently prepare the student for the assignments. Watching them and reading the text will not give the student the skills to solve the assignments, forcing the student to search online for a better tutorial. I recommend providing the complete code to create a MLP, CNN, SWEM, RNN, LSTM and GRU.

With a basic template created, the lab questions can then have the student change epochs, batch sizes, etc.

I am aware that some basic templates were provided, but providing a SWEM and having the student convert it to a RNN is a huge jump. I took detailed notes during lectures and read the lab notes, but ultimately had to find other resources to complete the assignments, because the answers were not provided by this course.

I hope this critique does not come across as a personal attack. I teach electrical engineering and understand how difficult it can be to fully explain complex topics. Thank you, all four of you, for creating this course!

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 !!!)

Course gives us the fundamental knowledges of Deep Learning (mainly) and Machine Learning, I see it very clearly and easy to understand, the Instructors are very dedicated, especially Larry . But the disadvantage of this course is that you maybe gain nothing about Pytorch, as all the lab/assignment are optional. Yeah, there are no practical lesson at all!

If you are the type of learning-eager, this course is also good for you. But if you want more pressure in learning that needs you to exert, I will not recommend it.

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.

A simple intro to deep learning concepts, good for beginners. The code exercises are also a good springboard into using PyTorch, but they could've matched the lectures a lot better. For example, I would've liked "Build a NN from scratch" instead of just "Use this library".

Great content, but would have been nice if there were downloadable lecture slides for all lectures (not many for Week 4), and if some of the broken links in the labs (e.g. for downloading anaconda) were fixed, as well as bringing the labs up to date.

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.

A good introduction to Machine learning, literally!

Wonderful experience and learning environment.

GOOD COURSE

I like Coursearas courses, This course is also very well, I clear mu many concepts from here.

But the issue I find with this is that the lab activity is not organised in well manner. Because I can't understand the codes because of that I can't do code by myself. Reason is just no one can understand the code who are not familier with some new modules of python. So if lab activities are organised in also same vedio type material, where a mentor teach about that how that code is work, this is better idea I think so.

I hope you will work on this and provide me a vedio session on coding parts.

All other things are too good mentor, there ppts and there way to explanation are fabulous.

Thank You