The assignments covered only the basics, without aspects like overfitting detection, hyperparameter tuning, ensemble learning, clustering, dimensionality reduction, missing data imputation, and cross validation.

I got a great understanding of the concepts but, there should have been more videos related to the implementation(coding) part. There was just once use of Third-party tool for every module and nowhere else...

Content was good, a bit shallow on some aspects (didn't cover many ML techniques, was light on SVM content, etc.). But the quizzes were too easy and didn't properly test technical aspects of the course.

The sections on Clustering and Recommender Systems were difficult to follow. It would have been ideal if they had more in-depth video explanations or if the contents in the lab notebooks was simplified.

It is a really good course for understanding theories and covers vast topics! The concept were explained very nicely but it lacked proper mathematical working of algorithms or deep intuition about them.

There is more theory than practical examples and exercises. The final project is nowhere near the actual course syllabus. No ML algorithm is taught in the course. But you ask them in the final project.

The instructor is very good and explanation of concepts is very clear.

But the code explaination is not there so we have to search for each keyword on google. Just wanted to have someone to explain code.

not deep enough, many algorithm and model evaluation approaches that wasn't include in this course. But I think for beginner who just get into a Data Science or ML things, this is a good modules

Its Ok. From Theoretical aspect, its good as a introduction. But for Python, this is not like introductory. Python programming is just reading materials. There are no videos for explanation.

So much stuff skimmed, left unexplained. Explanations are very shallow. This course gives you an idea on what you will have to do to tackle ML learning, but I can't say it fully teaches it.

Good explanations in the video, however the complementary notebooks are lacking in depth explanations. The capstone project is underwhelming, as it only includes classifications algorithms.

Was very hard with the algebra. Most of the time they explained the formulas and I was lost. After that they said "This is not mandatory because it is already in the NumPy / SciPy library"

A really good course, until you get to the final project, which is terribly written. It is unclear what the actual objectives of the final task are supposed to be, and the English is poor.

Really interesting subject, but the course material was just insufficient for beginners. The new codes were not explained. Out of all the other courses, I learned the least in this one.

the course content is good, the course exercises are great. But there is no responsible human TA monitoring the discussion forum. So if you get stuck in a problem, then good luck to you.

I understand that you want to cover many methods in this course, but you see so much that it is confusing, difficult to assimilate. I think this is the least good of the entire series.

Assumes a lot of background knowledge around Python. Not a great introductory course for someone with no experience with machine learning, AI, and limited experience with coding.

Generally good course with essential regression, classification and clustering algorithms. Some snippets of code are a little outdated and could be replaced by neater ones.

Contents in labs were just there without explicitly explained in detail. Few codes were written without proper understanding. Overall for understanding, it was good course.

The course has some valuable pieces of information to whom have not any background about Python and machine learning. Highly recommended for beginners, not professionals!!!

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)

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.