I want to give this course a higher score because I do think I learned A TON. However, I learned a ton because the course had some flaws in instructions and assignments that required some frustrating moments and a lot of outside work to correct. If you take this course, DISCUSSION FORUMS are a must because of all the errors and bugs in assignments. The explanations are a little 'too rosy' in the videos in my opinion (they show best case scenarios) so there's a disconnect in what i actually had to do to pass the assignments which tended to have lots of room for improvement. That said, if you are willing to go out on your own and figure it out (mentors are so-so in actually helping), then this course is a great ML workout!

The course is so informative and interseting.

Glosses over material (much like prior courses in this specialization), the professor is audibly nervous during recorded lectures, and many assignments require information and functions not covered in the lectures. Additionally, out of date Python modules are used in the notebooks, so you're learning often deprecated usage patterns, not to mention the constant struggle that is the auto-grader. You can teach yourself with free resources and save yourself the money and unhelpful bouts of rage against the auto-grader.

This is a great course for those with limited experience of machine learning, wishing to quickly grasp how to apply machine learning methods and get their hands dirty. In my opinion, this is the best course in the specialization so far and as in previous courses you are expected to dig into further theoretical/usage details yourself from online documentation (hence the name applied). Concise lectures and interesting reading materials, as well as hands-on assignments. My recommendation is to either start with this course or take it together with more theoretical courses (such as "Machine Learning" from Stanford or "Machine Learning Fundamentals" from UCSD) to get the full flavour of what machine learning has to offer.

어려웠어요 ㅠㅠ

There is a huge difference between teaching / tutoring and just reading some pre-written scripts. Even on an online course. Andrew Ng's Machine Learning course is a great example of teaching and this was one of the worst courses I have ever taken in coursera / udacity.

A lot of stuff, compressed in a short time. It's more about memorizing a lot of concepts rather than understanding them. I strongly recommend to take the course of professor Andrew Ng before this one.

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.

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.

My biggest critique of this class is that it is not challenging at all. Homework assignments are just a repeat of the lectures and take less than an hour if you took notes on the lectures. In other words, there is no value in the homework assignments.

The first two courses in this specialization were awesome. We did real life examples for homework assignments and through research you learned more than you had asked for. It was perfect.

Even in lectures, there is nothing 'applied' about this course. The professor just covers the content with no real-life examples. Very mundane and unexciting.

Also, why not talk about multi-label classification? Professor takes a real example with multiple labels (handwritten digits), makes it a binary class and then proceeds to explain it... Thanks.

My recommendation would be to restructure the homework assignments. Instead of having 7 questions that spoon-fed you the solution of a primitive problem, ask us to do some Kaggle challenges, or give us a topic that we go out and solve, do some peer-reviewed assignment. Lastly, if you don't have time or don't want to explain important concepts like pipeline, nested cross validation, and multi-label classification, add them as resources.

I am NOT confident in my ability to solve machine learning problems in Python from this course, nor is this course worth recommending.

Not very good compared to the first two courses :( :( :( ... I took a Machine Learning Class from Stanford which was incredibly well put together and presented (though to be fair, it was 12 weeks), but it was in MatLab and I wanted to take a course in Python just to have a different perspective and solidify my understanding. Unfortunately, I find this course to be confusing more than anything. If I hadn't taken the Stanford course before, I'd be completely lost. It's very dry, dense, and hand-wavy and doesn't go into a whole lot of details with anything leaving you wondering what's happening and why and how... I don't approve of jumping straight to using the built-in functions if you don't understand the processes behind them (which I personally don't have a solid grasp on them still) ... I think they are just trying to fit too much information into four weeks and it's really lacking. Maybe if you're already familiar with linear regression, it's not as hard to follow. Either way, I'd recommend either taking the Stanford class first, or learning about this stuff elsewhere before starting this course.

A very practical course and it helps me to understand more about machine learning theory. After all, this is a great course. Thank you.

Great course! Very helpful.

Some concepts should be introduced in detail.

Dude

Really well explained. Great excersices! Well done!

Initially i had issues in getting in to video learning mode, got accustomed to it. One of the best way to learn in your own time as and when it suits you. Submission issues got sorted when discussed with peer. Maybe a SPOC for each course can be of more help to do it more quicker.

An excellent program on applied Machine and highly recommended

A Great course, the extra offered learning material helped me out to dig deep into the course

Very Informative Course

This is the worst course in the specialization. The autograder is bad. There is inadequate explanation about when to use the different models. Presumes way too much about the student's level of knowledge. Would not recommend.

Excellent material for study

This is an OK introduction to Machine Learning. It covers a range of relevant topics. The gap between the lecture content and the assignments is the typical chasm for this U.Michigan "speciality", and frankly you end up basing assignment answers more on internet research rather than lecture content.

I'd sum it up as a substantial missed opportunity. The last assignment is really good in terms of doing a realistic Machine Learning project, but the preceding course content doesn't give you the tools or frameworks to do that project in a logical, industry standard workflow. It gives you an idea of what the tools are, but not how to really apply them all together in an efficient and logical series of steps.

It's as if those who designed the course decided that learners needed a tough-love approach, like a trainer lying down on the grass and showing learners swimming strokes, and then just throwing those learners into a pool and expecting them to keep afloat, and combine what they remember with what they see other more experienced swimmers in the pool doing. It shows a fundamental misundestanding of the Coursera learners usually being very time poor and expecting much more from the instructors.

Great Content, Great Delivery, Thumbs Up!!

The auto grader should be error free