Sep 09, 2017
This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses
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!!
교육 기관: 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 Additionally, out of date Python modules are used in the notebooks, so you're learning often deprecated usage patterns, not to mention the constant struggle that is the auto-grader.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.
교육 기관: 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!
교육 기관: 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.
교육 기관: Lin Y•
Jul 09, 2019
This could the single most interesting course amongst all the 5 courses in this specialization. It made Machine Learning easy to interpret and fun to explore for beginners. The assignments are very thorough, though with some autograder issues. I strongly recommend anyone who's interested in ML to take this introductory course to again some knowledge in the different methods and applications of ML in various fields.
교육 기관: 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.
교육 기관: solarmew•
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.
교육 기관: Athira C•
Jan 30, 2019
The course is so informative and interseting.
교육 기관: Haim S R•
Jun 27, 2019
Gives practical experience with ML in Python.
Hides the math under the hood :(
However, this course is not enough to become a real data scientist. One needs much more exercises.
교육 기관: Choi H•
Nov 23, 2018
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: 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.
교육 기관: Arpan S•
Aug 04, 2019
I am unenrolling because of the following reasons:
1) instructor lead training is very very boring - the gentleman keeps talking in same pitch and there is no lucid explanation behind the math that is constantly thrown at you
2) the course does not bother to put in any real world scenarios to correlate the content with
Overall really poor experience
교육 기관: Romel C B•
Dec 19, 2018
Me gusto muchísimo, los temas, la dinámica para el aprendizaje y el trabajo final
교육 기관: Michael T B•
Dec 19, 2018
Great class! I had fun learning many new things in this course. The professor did a very good job at taking a complex subject and making it simple and easy to understand. The code and assignments were straightforward and not overly difficult. The real quizzes/tests in this course were appreciated as this felt more like a "real class" where one can really learn a lot. One of the best online classes that I have taken.
교육 기관: George G•
Dec 23, 2018
교육 기관: Angelo S•
Dec 21, 2018
An excellent resource to immerse yourself into machine learning methods. Professor Kevyn explains key concepts in the most intuitive way possible. It does require some previous experience in Python.
교육 기관: Akash C•
Dec 09, 2018
교육 기관: TAIBUR R•
Dec 11, 2018
i love this course
교육 기관: Daniel R•
Dec 12, 2018
Wonderful program, great teacher. Learned a lot and have used a bit in the real world!
교육 기관: Phillip L C•
Dec 25, 2018
Great course - balanced and very revealing for direct application.
교육 기관: Nithin R•
Dec 25, 2018
contents are good.
교육 기관: SURENDRA O•
Dec 25, 2018
The course was very well designed. The pace of the lectures are perfect unlike other course when the instructor moves very fast.
교육 기관: Megan J•
Dec 31, 2018
In depth understanding is required to complete the assignments. Challenging without being demanding.