More peer graded assignments should be there , so that learners get more practice for building models.

Great overview for many of the key Machine Learning algorithm types using in Python for Data Science.

Great course for the fundamentals of different machine learning techniques. Enjoyed this wuite a kot

Recommending more emphasis on the coding behind the algorithm (reminders / links to references ...)

This was a good course - but still could use more hands-on exercises to go along with the lectures.

it was a nice course but there must be a little explanation video of the codes written in the labs

Would've liked the labs to be a little less demo and more DIY, but otherwise outstanding material.

Covers all the basics of sklearn library. Would have been nice to have more assignments/practice.

I am enjoying the course so far. Very well explained with a pretty comprehensive course material.

it was a nice course giving basics of every ML algorithm and i am all in all very much benefited.

The course is pretty good, I just hope the printing mistakes in the slides will be corrected soon

The underlying concepts of the various algorithms were broken down and delivered with simplicity

Excellent course explaining all the essential details to kick of the ML journey using Python.

Difficult to understand for a Non-mathematical student. Overall a great learning experience.

It was a good course but the final exam could do with more structure around what is expected

Comprehensive, good to understand, minor errors in the description of the final assignḿent.

Very eductational as a intro to machine learning - wish the homeworks were more difficult

the final peer review quiz was too difficult, should have included more practice exercises

This course is very fruitful and it's instructors are awesome. Thank you Coursera and IBM.

Great for beginners. Explains theoretical concepts well but lags on the coding part of it.

It is such a vast topic, It would have really been great if there were few more exercises.

Random Forest, XGBoost etc should have been there. Decision tree explanation was amazing.

A little bit to basic for someone who studied the topics in the past but a good refresher

Hace falta que se explique mas los cuadernos de laboratorio para entender mas la teoria

I recommend including more examples and documentation of the metrics in the algorithms.