This course is definitely not for starters. People should have good knowledge before enrolling in this course and then this can be taken as an excellent refreshing course.

The Labs and assignment had poor instructions that were not easy to interpret. Some of the videos need to be reviewed as they do not match up with the transcribed texts.

A lot of theory, not a lot of examples. The final project had lots of typos, pre-written code needs updates, questions need some clarification. Theory was fun though.

I feel like the course started in the correct direction but then moved very quickly over some complex issues (i.e the programming behind building the ML models)

A bit better than the previous one but still lots of room for improvement. Inconsistent use of terms, typos, mediocre slide decks are still a big problem.

The explanation for each algorithm was good. However, the labs and the last assignment does not really explain what to do and it is all over the place.

Loved the assignments out here. They are awesome. Anybody who knows a little python and dataframe manipulation should be comfortable with this course.

It could be more didatic, with more simple (and ready) codes, and also a step by step code block composition to explain better each part of it.

No Practical Videos on applying Algorithms. Just explaining algorithms. Kindly add practical videos as well. Else, the course is fantastic 👍👍

A good course! I think it would have benefitted from more explanation of the code in the videos, rather than just jumping into it in the labs.

The course is pretty descent but it doesn't teach you how to use python it just give documentation and you have to read it and learn from it

It is a very brief course, not recommended for computer science students. If you're from a non-cs background it will be fine for a start.

The course gives idea about the things to know choose a prediction algorithm, only thing is - the coding part can be stressed upon more.

It covers only the basics of machine learning not all topics are covered in this course. You will need to learn many things on your own.

It would have been helpful to have an explanation of t each of the lines in the code, especially the ones that created the graphs.

I learned a lot but wish the coding was explained better. The final project could have been better if it had more instructions.

Need more clarity while explaining the algorithms. Also need video lectures on the code used in the lab and how the code flows.

Peer review was problamatic , IBM Watson was tough to grasp could have been more informative .

But the course was really good

This course covers the basic of major algorithms. It could be useful if they add more examples and more metrics calculation.

Why am I not eligible to upgrade to a New version of a specification if it exists and I already paid for the next months??

Basic Mahcine Learning course. It goes through the very basics of several models, but lacks practice and true excercises

Was not able to get a clear understanding of the applications of Statistical concept applied in different ML algorithms.

The course is good but a lot of content is just thrown at you without explaining anything the labs need to be improved

Some useful material, but again plagued by bad spelling, punctuation and technical issues. Nowhere near good enough.

a very short video on theory part and without practical example. then we directly jump on notebook assignment.