Very happy with the course content.

On the other hand, certain instances need to be updated/corrected.

For several assignments, the files don't load and you need to dig through the forums.

It would've been 5 stars otherwise.

It can be more detailed. It is on broader terms only. I will recommend Andrew Ng ML course to do as well because it covers too many things than this module. Otherwise, this is a good module as well. :) Enjoyed doing it.

Good Introduction Courses, but need more guidance for assignments as there is a gap between two of them. Assignments do need some more hours to finish. In all, a great course for anyone to break into machine learning.

Challenging and fun course. The number of topics is on the high side. Maybe break this into 2 courses? The programming assignments are fun. You will need to go to discussion forum to solve often encountered problems.

Much better than the second course, the materials are carefully prepared and organized, teaching staff are very helpful in solving issues, however, assignments are not so challenging, still needs improvement.

Comprehensive and interesting course in Machine Learning. The use of Scikit Learn helps to give a concrete understanding of ML as well as how many specific algorithms can be utilized in real world problems.

It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.

assignments were so good. I think there was not enough information given for the quiz tests. And also the code given was not properly explained. But the materials were so good for practice

A good introduction to algorithms available in python. I didn't give it a five stars because I 'm still confused on which algorithms to pick/use when I want to work on real data problem.

Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.

Globally, very good course - strongly recommanded

The final assignment passing was a little too east,

there not being need to use fully what I learnt.

Still,the overall course was very good, and I am willing to keep on take other courses.

This course was very good, with a lot of information and important tips for me. The instructor is good but he is long winded, so this course was very long with videos during 20 minutes.

- more technical materials, comparisons and better classified details should've been provided, especially to be more proportional to the assignments.

-again, subtitles were full of typos

Outstanding course content and curriculum for intermediate learners. Learnt some basic applications of machine learning in detail. Enjoyed while working on assignments and graded quiz.

The course is well balanced but the progression becomes quite agressive at Week3 and culminate at Week4 with a real life case assignment without much guidance. Great experience dough.

Amazingly explained. An intermediate Machine Learner would definitely get clarity of concepts already learned and also new concepts explained so skillfully with graphs and diagrams.

Good overview of methods in ML. Would have been nice if the lectures contained a little more mathematical rigor and explanation of why and how the various algorithms are effective.

Good course and prof.

The exam and exercise in very interesting according to what I learn in following all videos, with this i improved my level in python progamming, I recommended.

Automatic assignment grader has room for improvement. Some python code that works perfectly well when run locally or on the course web page would crash when run by autograder.

The class material is well prepared and make machine learning very easy to learn. The first three homework assignment is a bit hand-holding but the last one is really good.

Good Course, i would have liked a little bit more theory about the algorithms, but this is an applied course of ML. Projects are good and the readings are interessting!

Videos are good and had challenging assignments. I enjoyed learning new concepts. I wish we had one more week to practice more on advanced Machine learning concepts.

Very interesting, a lot of focus of statistica theory and little less (as compared to previous courses of specialization) on practical examples and implementation.

Good overall but some concepts and python functions need more explanations. Maybe 5 or 6 weeks are more appropriate for this course. It is too dense under 4 week.

better than the first two courses of this specialization for the content is coherent and the assignment is relevant to the knowledge taught in the course video.