SZ
Dec 19, 2016
Great course! Emily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.
BL
Oct 16, 2016
Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much
By Dillon D
•Sep 4, 2016
Very informative and make the machine learning experience much easier for a beginner to all these new concepts. This course is very well set up to help students into the future apply there new knowledge. Only thing is the software was a little difficult to at first get working on my mac but other than that everything was fabulous.
By Mohamed G M S B
•Sep 2, 2018
I would've preferred if the used tools were opensource. Also, I felt that in many videos I lost my concentration due to the side comments that had nothing to do with the actual technicalities of the course. Nevertheless, the material presented in this course provides an excellent overview for the foundations of machine learning.
By Igor B
•Dec 29, 2016
The course was very well taught and the exercises provide a realistic introduction into real-world problems. The only thing that is missing to get to a 5-star rating would be to use standard machine learning libraries (scikit-learn, which is free) instead of GraphLab Create, which requires a paid license to be used commercially.
By Vijay V
•Jan 29, 2016
Great Introduction to Generic Machine Learning Concepts.
One suggestion to the teachers would be to include an optional programming section just to introduce GraphLab to users. There is a lot of API calls which are explained on the go but a high level view of the library with the relevant structuring of APIs would be helpful.
By Abubakr M S
•Nov 11, 2018
This course is very informative and useful for anyone who have no machine learning background. The case study approach helped a lot in understanding the core of every concept before deploying.
The only drawback was that there was no tutorial on how to install the software which was so tricky and take me ages to install it.
By Sparsh K
•Sep 1, 2020
The course is pretty decent but what i really didn't like was it outdated use of software and pretty less efficient mentors.I suggest, to please moderate this course, this course is indeed a good one but need to be supplied with new references and less dependent on particular libraries.Otherwise the course was great.
By Jarred N
•Nov 23, 2015
I think the course met my expectations – it's super high-level and does not at all go over the underlying algorithms involved. I give it 4 stars because I have this feeling like this specialization is an underhanded way to sell the Dato GraphLab Create product. There's a bit of a conflict of interest going on here.
By Kumar N
•Jul 30, 2020
Wass a great introductory course. Definitely recommend for starters. The course was well constructed and presented. The only problem I faced was from the software side. I was having a hard time installing and importing packages, those are not covered in this course. I like the case study approach as an introduction.
By Elena I
•Nov 25, 2018
The course has everything you need to get an overview of machine learning. It's perfect to understand the purposes and techniques used. However, I'm a bit concerned with practical tasks, since they heavily imply on GraphLab create, and this is a serious disadvantage, since one will barely use it in future.
By レンユー
•Jul 22, 2018
This course is great if you just started getting into the field of machine learning. (Great if you have no or limited programming background)
Pace is a little bit slow and Programming assignments does not captures algorithms discussed in lecture.( Although it mentions, it never let you implement yourself.)
By Lennart B
•Feb 7, 2016
Very good introduction to machine learning, quickly enables the student to perform regression, classifications, etc. but it would be nice if the course went into a little more detail, the quizzes are very superficial. It would also be beneficial to explore examples of applications across different fields.
By Forrest G K I
•Aug 15, 2018
I enjoyed this class. It does provide a good over-view of the different machine learning algorithms and their practical applications. My only qualm was that the programming assignments seemed somewhat irrelevant as the underlying structure of the different machine learning algorithms had not been taught.
By sarathva v
•Nov 11, 2019
Nicely covered basic ideas about different areas in ML . Hans-on sessions gave a very good idea to solve ML problems practically. Theory explanations where good.
One suggestion i had is about tool used it would have been cool if course was with scikit learn and pandas, since many companies use the same.
By firstin l
•Feb 12, 2017
I really enjoyed the overall materials and especially loved the way they split the course into two sections:Theory and Programming.
However, i wish they were using more standard packages such as pandas, or skit-learn instead of graphlab. It was a good class to taste what is going on in the world of ML.
By Michael R
•Jan 18, 2016
Great introduction to machine learning concepts with nice assignments. It seems there needs to be some cleanup performed so that the lectures and content match up a bit better. Overall a useful and approachable course to motivate the need for additional study in the rest of the "specialization."
By Jane z
•Jan 8, 2020
I really enjoyed this hands-on course with a lot of practice. The difficult part was the week 1 when we had to set up the virtual environment, and pass the first quiz. I believe that if there is more support at the beginning, more people would have stayed on to finish the course.
Thank you!
By Andrey Y
•Dec 28, 2017
Assignment instructions are not very clear and often not formatted properly - multiple questions are "glued" in a single block. It would be good to spend more time on GraphLab API at the beginig of the course. iNotebook did not work on coursera.org, I had to install a local version of Python.
By Wenersamy R d A
•Apr 8, 2021
The course has a really interesting approach, and I have enjoyed it, but as Turicreate (previously Graphlab) has not become a mainstream tool (on top of the difficulty to use it with Windows), I would rather have also some exposure to other tools, as Scikit-learn and Tensorflow, for example.
By Ayush K
•Sep 2, 2018
Case study approach is really helpful but we need to understand the formula behind those deep learning scores which i think has not shown in this course. Second, if you can provide more videos for coding then it will really helpful to do the "Programming Assignments" which i think is tough.
By Nick B
•Jan 29, 2016
Well designed and executed in the main. As videos are recorded once but viewed thousands of times it would have been nice if they had taken more time to write a script and look more professional - also mistakes in the videos that don't match the current material are few, but very annoying.
By Rohit K
•Nov 16, 2016
Good Course for someone wanting an overview of techniques. You would not be building something very cool after this. Course assignments are not very challenging, some good questions must be included.
Some mathematical part should be there. Expecting it in other courses in specialization.
By Mascha L
•Jan 24, 2016
I found this to be an excellent course. Both the instructors are excited about the work they are doing and do a good job of teaching the materials. I don't have a statistics background and college calculus is more than a little fuzzy. I was still able to understand most of the course.
By Manish S
•Jan 15, 2016
Pros:
1. Practical and hands on approach
2. How multiple problems like prediction, sentiment analysis, text retrieval etc can be mapped to a common ML model
Cons:
1. Uses DATO toolkit which is a licensed tool.
2. Maths behind any used technique is not discussed, which I think is an issue.
By Sunghyun H
•Dec 29, 2015
Very nice for those of people who want to learn the concepts of machine learning. However, libraries that used (which are not pandas and scikit-learn) was not satisfying for me. Googling about those libraries was totally difficult while there are lots of documents using pandas and scipy.
By Alec K
•Dec 21, 2015
A great high level overview and introduction to machine learning. The topics broadly cover different machine learning algorithms and their approaches. The only downside is that the proprietary library they use to teach the concepts is incredibly expensive for an individual to license.