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 Some snippets of code are a little outdated and could be replaced by neater ones.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)

El curso aborda muy bien el aprendizaje automático Supervisado pero no dice nada del no supervisión, además faltaron temas como XGBoost muy usado ahora.

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.