Aug 14, 2020
recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course
Mar 01, 2017
Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.
par Sheila B•
Aug 09, 2018
I've been working my way through the whole track, and this was by far the most complex material--but it was easy to understand because the videos were so clear.
I do have one bone to pick, though: the quiz material relies on very old packages. Again and again I had to finegle something so I could answer a quiz question. That makes you guys look like you are lazily sitting back collecting money but not really doing your job as far as teaching goes. It's time for an update. How hard is it to run your quizzes on updated packages and offer answers that are current?
Aside from that, I find that you explain material very clearly and you are my first choice for picking up a new data science skill.
par Andrew K•
Mar 13, 2017
So why four stars vs five stars, of all the Data Science Certification courses that I have taken: i) some of the examples and quiz challenges don't work as they should, ii) Machine Learning is rapidly changing area - should be updated to reflect this and perhaps a high level taste of Deep Learning, iii) posting the Final Project is overly complicated relative to methods of the other courses - this should be cleaned up - still not clear how point to a github repo link and also have a rendered html page working from that same link - requires two links to present materials and must use default names like index vs. a project name.
par Terry L J•
Nov 09, 2018
Lot of good material, however, on all of these courses, it would be very helpful if they were better organized for learning.
Overview of learning objectives in a step sequence for a more organized approach for learning (maybe even a process roadmap map sequencing activity to follow that you can reference back to.
Detailed information for each step in the learning process that can be followed that maps back to the roadmap.
A summary of the learning objective in the roadmap sequence.
Basically, just like writing a paper, > overview/objectives > Main topics >subtopics, etc. > summary
par Chris M•
Aug 14, 2016
Unlike the rest of the modules in this specialisation, this one was well taught, a good blend of theory and practice and well paced.
There were still a few issues with wording in quizzes (and some where there seemed to be two identical answers to one question, where one would be considered right and the other wrong - purely chance). In addition, the lack of consistency in how to submit assignments across the specialisation is frustrating, I'm not sure if it's supposed to be a way to show how to use github or something like that, but it shouldn't be the case.
par Carlos M•
Jul 12, 2017
A good course, but the field is so large and so important. You'll really need the "hacker" mentality to get through this course. They DO NOT teach you even close to everything you'll need to complete the course. It's also very stats/math heavy which will make the theory difficult. This isn't why I only rated 4 stars. I did so because of the lack of Swirl and the feeling that I still don't feel like I understand the topic well enough to do anything in a business setting yet. I was hoping for more from the class.
Feb 03, 2017
By using the caret package, this course took a very pragmatic approach towards machine learning. It demonstrated how to perform all the essential tasks in making the machine (algorithm) learn from data.
In my case, this course required a dedicated time commitment for successful completion. In addition to course lectures, i used the 'Machine Learning with R' book to fill my knowledge gaps. Overall i feels that this course helped me in my journey of gaining a better understanding of this subject.
par Yukai Z•
Dec 09, 2015
A good introductory course for people who has an interest in knowing the principles of machine learning and want to make a step forward. Sufficient details covered throughout the course and additional resources were provided which are very useful. Quizzes were well designed with minor improvements in the accidental mismatch of the answers due to package version issues. Overall the study experience was enjoyable and would definitely recommend to someone who wants to start knowing data science.
par Romain F•
Mar 22, 2017
Good course on the whole, learned a lot and enjoyed it, but it would need to be updated and corrected (certain bits of code don't work as they did when the course was produced, which can be pretty confusing). Would be nice also to add some more content at the end of the course : the lecture about unsupervised prediction felt rushed, and a proper conclusion opening up to the rest of the field would be useful. Anyway thanks again for this wonderful learning opportunity, keep it up ! Cheers
par Carlos S•
Jan 31, 2016
First and foremost I'm so thankful for the exposure to so much material in such a condensed schedule. Very good class. Even though I had to muscle my way through it.
I think the class could be improved with one additional discussion thread for the project.
A guide similar to the ones created for Inferential Statistics and Regression would also have been very helpful.
I benefited immensely from reading parts of the book "An Introduction to Statistical Learning" while taking this course.
par Robert K•
Nov 14, 2017
I realise that the course is practical machine learning, however I find myself wondering more about the 'whys' than the 'hows' after the course! Still, much benefit and many useful concepts covered which can be revisited in greater detail down the track.
I would also like to see the final assignment change subtly every so often as there are existing completions on the web and it's too easy/tempting for some to simply copy and paste.
par Vathy M K•
Aug 13, 2016
It's very cookbook driven - it's not a deep dive into the topics. This can be dangerous: a little knowledge and all that. However references for more are provided. If you can imitate the coding examples, you should be OK for the assignments. Fair warning: the quizzes are hard to replicate unless you set up your environment to mirror exactly the version of the packages used in the course.
par Siying R•
Nov 27, 2019
This instruction is better than the last one because he can use examples that people from outside the medical world can understand. The quiz is harder than the final project. It requires students to do extra work to figure things out. I see the pattern where the instruction really is the door holder to you and you need to walk in the room and find what you need.
par Jikke R•
Aug 11, 2016
Very enjoyable and generally quite understandable introduction to machine learnings with hands-on approach through the course project. It was a bit too fast-paced and generic for my liking, but many options were offered and highlighted for finding additional learning documents and courses to be able to deepen the knowledge acquired in this course.
par Sean Q Z•
Dec 11, 2016
As the title states, very practical way to show you how this is done in R.
Most of them are lines of codes and some explanation. There are tons of details behind that and remains un-explained.
As other courses in the specialization, students need to do a lot of self-study to further understand machine learning.
But at least, learned a lot.
par Charles W•
Dec 08, 2019
I think some material might need to be revised, but I thought it was very interesting to see everybody's model building code (and perhaps that can also help me in the future).
While it is mixed with other notes, I have more detailed thoughts in this blog post: http://cdwscience.blogspot.com/2019/12/experiences-with-on-line-courses.html
par Jorge E M O•
Sep 07, 2018
The course rushes over a lot of concepts and it already shows its age - however, it's a pretty solid introduction to machine learning from a practical perspective. It will provide you with a lot of ideas for further investigation and exploration and in the end you'll end up with a wide vision of the machine learning process.
par Brandon K•
Mar 30, 2016
The lectures were great and engaging. I felt like they went too fast. Jeff says at the beginning that this is just an overview and points to some other resources. As an overview, this class works well. You can expect to learn a bit about what machine learning is and how to to do it using the caret package in R.
par Oliver S•
Jul 26, 2019
A reference solution for the quiz questions as there are in some other courses in this specialization would have been nice, since I got sometimes very different results using the newest versions of the libraries and I'd really like to know, if I made any big mistakes and it's not only because of my setup.
par Lukas M•
Oct 06, 2017
The lectures are very good to get the basic knowledge about machine learning. One suggestion is that the lectures can be longer, covering more detailed stuff and a little bit more advanced materials. Moreover, some codes are not explained clean and clear for me. Hope it would be better in the future.
par Robert S•
Sep 16, 2019
The lecture material is great, but the quiz material is in need of updating. R and it's packages have gone through many updates since the problems were written so it is sometimes difficult to reproduce their results even with running the sample codes given after getting the answer correct.
Jun 03, 2016
This course allows you to implement practical solutions using machine learning algorithms without having to know the mechanisms behind the calculations in detail. Unfortunately questions in the discussion forum were quite rare and many questions were not resolved during this course.
par Swapnil A•
Jun 09, 2017
The course covers few important topics in R like cross validation, decision trees, random forest etc. which comes in very handy for a data science aspirant. It expects the participant to have a descent knowledge in R. Overall, I am pretty satisfied with this course. Thanks!
Oct 25, 2017
This course is brief but it has the 2 best ingredients for having a really decent first step in Machine Learning:
1) It covers a broad group of different algorithms
2) It provides reference material for those in which you want to get deeper.
Really good job in this course.
par Yuriy V•
Mar 10, 2016
I liked the course and found it informative, but wish there were more stuff on unsupervised learning neural network algorithms (SOMs). Learning about most used algos are great, but would also like to know other machine learning algos that are used concurrently.
par Marcus S S•
Feb 25, 2017
Great course! The hands-on approach make it very useful for one to start doing some very interesting analysis in real life! Thanks a lot! You guys could only make some efforts in updating some classes and packages used in quizzes. But the rest was great!