Jan 17, 2017
Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.
Aug 25, 2016
excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.
By Ernie M•
Sep 25, 2017
I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.
Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.
Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.
Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.
By Eugene K•
Feb 10, 2017
If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.
Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.
Jul 08, 2019
I like the course very much. I learnt so many advance concept and real life implementation.. but slightly disappointed by the quiz question please be specific what you wanted us to answer. looking forward for SVM and deep learning material.
By Tsz W K•
May 15, 2017
The materials presented are excellent with well prepared skeleton codes for all ML models. Comparing this course to its three preceding ones, this course is more challenging both conceptually and computationally. The slight drawback is that, because of the highly technical nature of the last three weeks' materials, there isn't enough guidance about how one may construct the ML algorithms from scratch, that is, learners with less experience in computing will, more or less, have to accept the sample codes with little confidence about how to (re)write such codes in the first place.
As a result, I believe that learners with more experience in algorithms and data structure (or learners who proceed to learn more about this area) are likely to gain more from this course for at least two reasons: i) they are more comfortable with the complicated ML algorithms; ii) they can improve the algorithms to speed up the estimation time (some advanced techniques are quite computationally expensive, say over 20 minutes).
In general, I have learnt very much from this course and love it.
By Hamel H•
Aug 07, 2016
This course rushed through the material at the end.
By André F d A F C•
Jul 25, 2016
I found this Course less well prepared than the previous 3 modules. Misleading hints in the assignments, code errors, etc... Also, I found the amount of work required higher, which is not in itself a bad thing, just a bit unexpected.
By James F•
Aug 10, 2016
The course, and indeed the whole specialization, was advertised as not requiring the Graphlab Create toolkit. This is untrue, as the final programming assignment does require it. The general dependence on SFrame is understandable since it is open source, but requiring any interaction with a licensed product (even if temporary and research licenses are available) greatly negatively impacted my experience in this course.
By Somu P•
Nov 17, 2018
Excellent course, which gives you all you need to learn about machine learning. Concepts and hands on practical ex
Dec 19, 2018
Great but hard~!
By Martin R•
Dec 12, 2018
I'd bring the last summary video at the beginning (the great summary of all weeks of the course). This would outline the course evolution in advance and give guidance what's ahead. IMHO this would help to not get lost when drill down in a single section.
By Jay K S•
Jan 05, 2019
Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.
By KAI N•
Jan 03, 2019
Excellent course with great and reachable explanation
By Vikash S N•
Feb 03, 2019
It was great but I was also interested to implement the solutions with pyspark...though I did it eventually. Thank you!
By Manoj K•
Nov 26, 2018
session was very helpful & full with relevant contents
By Nagendra K M R•
Nov 11, 2018
Nov 11, 2018
By Susree S M•
Nov 14, 2018
This course is very useful to know about the concepts of machine learning and do hands-on activities.
By Zhongkai M•
Feb 12, 2019
Great assignments : )
By Edwin P•
Feb 15, 2019
Excellent, good contribution to the technical and practical knowledge ML
By Jialie ( Y•
Feb 21, 2019
The course is really helpful, though it would be better for teacher to illustrate the concepts by using examples, instead of abstract terminologies
By Sathiraju E•
Mar 03, 2019
Very nice course. Things are well explained, however some concepts could be expanded more.
By Akash G•
Mar 11, 2019
Machine Learning: Clustering & Retrieval good and learn easily
By PRAVEEN R U•
Dec 27, 2018
Nice content and well made presentations.
By Juan F H•
Nov 15, 2018
The teacher is awesome
By Feng G•
Aug 09, 2018
Emily is an extremely awesome instructor. For those who have some background in statistics, biostats , econometrics and math and want to study machine learning by themselves, these modules can be an outline that introduce basic topics in machine learning.
I'm looking forward to see more advanced courses in these topics from Carlos and Emily.