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.
Jun 18, 2018
Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.
par Jiarui Q•
Mar 27, 2019
It is still kind of hard for a learner to understand the methods. But it gives me a overall introduction of machine learning and I will have further learning in the future.
par Sakib S•
Mar 15, 2019
Include more swirl practice problems.
par Qian W•
Sep 09, 2018
need eva on my project
par Jakub W•
Sep 24, 2018
Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice
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 Grigory S•
Aug 28, 2018
A bit short on practical aspects of different models
par Chonlatit P•
Oct 20, 2018
GREAT course! There are all base of machine learning field. The limitation is blur between basic and detail especially maths. This course, sometimes , show the maths that make you confuse if you're not familiar with them.
par Johnny C•
Oct 23, 2018
It was in general nice course. However, quizzes need improvement.
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 Samuel Q•
Oct 24, 2018
Good course to get only the basics of machine learning. The assignments and quizzes are great but the lecture material is very brief and short. The references provided throughout the lectures are probably the best source of more information.
par Sulan L•
Nov 19, 2018
I hope we can have more détails in this cours and to see how to use the algorithms for the big data. Thank you.
par Diego T B•
Nov 07, 2018
Very useful. The models were very easy to understand
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 Rudolf N•
Dec 19, 2015
Thank you for inviting me to be a beta tester for Practical Machine Learning. I completed this course at the beginning of October of this year. When I was asked to be a "beta tester" I thought that I would be presented with new materials. However, the only thing that has changed is the look and layout of the Coursera web pages. The video lectures, quizzes, and assignment are the same as they have been for quite some time. Here are some specific comments:
1. The video lectures: To me, these are clear and easy to follow. However, like those in the other courses in the Data Science Specialization, this course covers a wide range of subjects but tends not to have much depth. When I compare this and other courses in the specialization to other moocs that I have taken including Machine Learning with Andrew Ng and the Stanford Online EdX Course Statistical Learning with Trevor Hastie and Rob Tibshirani, the somewhat cursory treatment of the topics in the Data Science Specialization becomes more noticeable. Perhaps in the interest of "truth in advertising" this course should be called "A Brief Introduction to Practical Machine Learning." In the interest of full disclosure, I should note that I have an undergraduate degree in economics and an MS and PhD in psychology with a quantitative bent. I have had lots of statistics courses, especially those related to ANOVA, MANOVA, nonparametric statistics, correlation and regression methods, and structural equation modeling. The latter is important in psychology because researchers in this field like to measure latent variables. I had been an analyst using SPSS for several decades and the courses in this specialization helped me to migrate to R. Also, there have been may new developments that have become more accessible through R packages (like the fancier tree methods) that were not available when I completed my PhD. Thus these courses (and others such as the ones by Ng and Hastie and Tibshirani) have helped me to keep abreast of these developments. So they are good for me, but I wonder to what degree do the courses in the Data Science Specialization actually make a person a "data scientist?"
2. The quizzes: I think these items are good practice and are at a reasonable level of difficulty. However, these items are the same ones that you have been giving for quite some time, with perhaps a few new ones added. A little googling will lead you to the answers to these quizzes posted online. I recommend that you put a little time and effort into writing all new items.
3. The final project: Again, this project is good practice and seems to be at a reasonable level of difficulty. And again, this is the same project that appears to have been given at the end of numerous iterations of this course. And again, numerous write-ups for this project can be found online. And again, I would recommend that you put a little time and effort into finding a new data set for people to analyze. This would help minimize some of the rampant cheating that I found in this and in other classes in the specialization.
On the subject of cheating, when I was doing the peer grading for the courses in the Specialization, I would enter the code of the students that I was grading into the Google search box and all too often I found links to submissions for the project by students who had taken earlier sessions of the class. That is, students were copying these earlier submissions by other students and submitting them as their own. And I don't mean that they were similar: students were copying other people's work line by line, character by character. I found that to be quite irritating and I always reported it to Coursera. Of course, if the instructors would change their assignments once I a while, then this sort of copying would be impossible. As it is, it appears that the good professors put a lot of time and effort into creating what are indeed a worthwhile set of classes. However, after they created the classes, they seem to have pushed the "autopilot" button and gone off to do their day jobs. I would suggest that re-engaging with these courses and reading some of the comments that other students have made would be helpful.
Overall, I appreciate the courses in the Data Science Specialization and specifically this course. I know that these class materials took considerable time and efforts to create. I wish the instructors continued success with these classes.
par vivek s•
Jun 07, 2016
introduces lot of machine learning techniques which are used by practitioners !
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 Igor H•
Sep 10, 2016
Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.
par Hernan S•
Dec 13, 2016
The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.
par Shobhit K T•
Dec 18, 2017
Great course with practical insights on machine learning
par Roberto G•
May 21, 2017
Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice
par Manuel C•
Dec 26, 2017
I feel I could have master the subjects better
par S M H R•
Feb 10, 2016
A good course where you can learn how ML algorithms work practically.
par Raymond M•
May 02, 2018
par Kevin S•
Mar 03, 2016
Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.
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!