lectures are boring, not updated but yes i learned something, but its not up to the margin

I had high hopes going into this course after the really well put together courses 1 and 2 in the specialisation, however the video material was dull and disengaging. Where the lecturer could have spend hours going into the ins and outs of how the different algorithms work, instead the course followed a structure of: 1 - Brief overview of an algorithm, 2 - whats the syntax in scikit-learn, 3 - what parameters does it take, 4 - what other commands are there

I was really disappointed, as most of the actual learning was done from reading other sources on the web and watching videos for free on YouTube. I guess the only positive is that because I paid for it I was forced to finish it?

worst course of this specialization so far , the instructor is just reading stuff not making any effort whatsoever and it seems like he's obliged to do teach this course ,the autograder is the worst and the journey with this course is really painful i hope that you take these points in consideration and just delete this course

The materials of this course is poorly arranged: how is that even possible to cover gradient boosting, random forest, neural network, and unsupervise learning in a single week?

This is a really bad quality course. A little bit more professionalism would be advisable. I will continue to the next course and leave this behind.

Faced problem in every assignment while reading the data sets. If the data is not in that folder what is the point of telling so?

1- very slow paced lectures

2- very basic and elementary examples

To sum up, it is boring and not useful for practical application.

I am not happy with the course material and the way teachers are teaching.

This is not a good course for beginners.

no teacher intraction

A very practical course for machine learning. By this course, one can get familiar with sklearn and pandas basic operation! The last assignment is a challenge for me. Thanks teacher for this great course!

Great course for the applications of machine learning. While I wouldn't recommend for someone with no ML experience, this was a great course for an R user trying to learn more python!

Very helpful and well-structured course, clear lecturing, and high-level assignments. I hope, however, if it can be offered another course specialized in unsupervised learning in ML.

Great Course. I love the way it is designed, delivered. I learned a lot. The most important part is that I enjoy every bit of the session and completed everything less than a week,

Course was designed in a well structured manner and the basic concepts were covered for Regression and Classification. Many many thanks to University of Michigan for creating it.

The course is awesome. Professor Kevyn Collins Thompson, explains the topics with examples in python which makes content easy to understand. It is the best course for beginners.

It is very good blend of study and practical assignment. Assignments were very well designed to greatly enhance the understanding about the things learned in the video lectures.

This course was challenging and extremely interesting. The long and detailed lectures and excellent lecture notes covered the material very thoroughly for an online course.

ML is a wonderful course.I learn new concepts with hands on experience.Each and every algorithm concept is clearly explained .I learn how to handle real time data set.

Very well designed courses! There are many materials to go in depth even if you have done Python Machine Learning in the past.

I​t's a really good course. I teaches you the basics of many ML algorithms. I really recommend it for begginers

My actual rating is 3.5 stars. This is the best course yet in this Specialization.

Pros: I prefer Professor Collin-Thompson's delivery compared to Professor Brook in the previous modules. I think he gives a good overview and sufficient depth for an applied course, compared to Professor Brooks which I find to be quite superficial most of the time, and weirdly detailed in other parts. Assignment is good enough for reinforcement learning and definitely better planned. I also appreciate the link to additional readings which are quite informative.

Cons: Assignment auto-grader. This is still the biggest letdown of all the courses in this specialization Codes which work on your laptop or suggested elsewhere on Stackoverflow etc fails to pass the autograder, so 30-40% of the time of the assignment is spent on wrangling the code to pass the autograder.

Note: If i haven't taken a Machine Learning course by Professor Andrew Ng, this course would definitely be much harder. This course doesn't go to much into the background knowledge,and they mentioned this many times. But I appreciated the applied aspect, since this was what I was looking for.

This course is an survey on how to implement many machine learning techniques using the SciKit Learn library. Following the course, you can learn several interesting details about how to work in the field, but it is important to take into account that it is not possible to learn the algorithms during the course, since a huge amount of material is covered during a short time; to make the most of the course you have to know them in advance. It bothered me to discover that the course was planned for five weeks but Coursera has reduced it to four, removing the possibility of practicing exercises on unsupervised learning.

Overall a good course; I learned a lot. But hard going at times for someone new to Python and Jupiter Notebooks. The time estimates for the module assessments are way under (maybe reasonable if you are already a Python expert and have some familiarity with the relevant libraries, but that's not my situation). File location mismatch between Assignment notebooks environment and submission / assessment environment was very frustrating.

Since there are many theoretical concepts in this course, like model evaluation and tuning parameters, it would be much better if those are explained using real or semi-real life problem examples. Especially the quizzes needed more context as to why a particular situatrion might occur, and why that particular variable of interest is necessary.