Oct 14, 2017
Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!
Aug 19, 2017
the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action
por Sarah H H•
May 02, 2019
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!
por Athira C•
Jan 30, 2019
The course is so informative and interseting.
por Brendan B•
Jan 06, 2019
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 Additionally, out of date Python modules are used in the notebooks, so you're learning often deprecated usage patterns, not to mention the constant struggle that is the auto-grader.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.
por Max B•
Jan 03, 2019
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.
por Choi H•
Nov 23, 2018
por SeyedAlireza K•
Nov 17, 2018
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.
por Riccardo T•
Sep 21, 2018
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.
por Raivis J•
Jul 27, 2018
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.
por Oliverio J S J•
Feb 04, 2018
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.
por Aziz J•
Nov 07, 2017
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.
Jun 13, 2017
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.
por Xinzhi Z•
Jun 23, 2019
Great course! Very helpful.
Jun 19, 2019
Some concepts should be introduced in detail.
por Harshith S•
Jun 19, 2019
por Lutz H•
Jun 17, 2019
Really well explained. Great excersices! Well done!
por Bharath R•
Jun 17, 2019
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.
por Sudharshana B B•
Jun 15, 2019
An excellent program on applied Machine and highly recommended
por Arpit S•
Jun 13, 2019
A Great course, the extra offered learning material helped me out to dig deep into the course
por Suleman k•
Jun 13, 2019
Very Informative Course
por Sean D•
Jun 12, 2019
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.
por Anurag M•
Jun 10, 2019
Excellent material for study
por Nigel S•
Jun 10, 2019
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.
por Anurag B•
Jun 08, 2019
Great Content, Great Delivery, Thumbs Up!!
por A. Z M R•
Jun 08, 2019
The auto grader should be error free
Jun 06, 2019
I think it gives a great overview on Machine Learning and Sklearn. Nonetheless i noticed it is less curated compared to the prevoius courses in this specialization (wrong filenames, unfunctioning links, old version of pandas respect the one used till now). Anyway it worthed and I'll give a look also at the optional unsupervised learning part