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.

Be aware that this course is from 2015. The videos are a good foundation, but they are old. The homework assignments use a proprietary python library (graphlabcreate/Turicreate) that is not useful outside this course. The more recent TuriCreate library only works on Mac. A Windows user needs to use older software. There is also very little activity on the forum - I see people asking for help, but no one replies.

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.

You will learn about Data Science and Machine Learning, but not much about Python.

The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.

There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.

I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.

This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.

I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!

This is a very good introductory class to regression. Even though I had taken already other classes in regression, like Statistical inference or Machine learning from Stanford, this course provided me much better understanding about the variance and bias of a model, as well as, how the the true error and test error is related. For some Quiz the result is different with scikit-learn than with Graphlab while the Quiz is prepared for Graphlab results. What is really helping is the notebooks provided to each programming assignment, so basically one need to write only a few lines of code when using Graphlab in order to pass the Quiz. I spent much more time making programs from zero with scikit learn (due to different results I gave it up in the last 3 weeks and used only notebook with Graphlab). Learning the usage of Graphlab is not so difficult, so I had no problem with that.

Very good course to understand the regression concepts like simple regression, multiple regression, lasso, ridge, kNN and kernel regressions. On top of that the course explained about the gradient descent and coordinate descent algorithm really well. The course is designed very well maintaining the continuity. The lecturer's pace and the explanations are very good and easy to follow. I recommend this course to anyone who wants to start learning regression.

I really enjoyed all the concepts and implementations I did along this course....except during the Lasso module. I found this module harder than the others but very interesting as well. Great course!

A very good course, but seriously outdated.

A great course that will take you way past what you may remember of linear regression from high school or college days. This course is part math, part algorithms and part application (in Python). I loved it. The instructors are good and the material is generally well presented (I took the course the first time through, so there seemed to be a few gaps / rough edges.)

This course may be intimidating if you don't like mathematical notation, or if you have never used Python before. It may also be challenging if your high school / college freshman calculus is rusty. The concepts aren't super hard (basic statistics, integration, differentiation, matrix math but with multi-variate twists), but you will need to think carefully through some lessons to appreciate them.

The online tests are good - and the instructions for each week's problems are detailed. There is enough guidance to clearly show what needs to be done, but enough gaps to bridge that you're made to think about the problem at hand.

I have taken so many other courses for Machine Learnig already but the way this course settles down all those concepts so nicely with visualisations and examples and with hands on practice assignments and problems is remarkable.

I really wish I took this course way back as its a must take course for beginners but I am really happy after finally completing this course and making the foundations more stiff and strong

Excellent course, the professors made it very easy to learn quite powerful technics like gradient descend and coordinate descend. I always saw them like black-boxes, but now, thanks to this course I not only understand how they really work, but I learned how to apply them to real data. This course was simply awesome.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

I really enjoyed learning through out this course. I did little bit struggle with Python but now I am a bot more confident to take on advanced programming in Python.

Thank you very much for offering course.

Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the assignments...it’s just that turicreate library that caused some issues, however the course deserves a 5/5

Very Informative and Technical Course...But lot of Mathematical derivations were too long. But very patiently explained.

I loved this course because of the detail understanding of the concepts. I was looking for a course which provide detail understanding of algorithms, and here I am. I am giving four stars for what has been given in detail, not five because I something is left ;) interpretation..

The program is well structured, the lessons are interesting and the hands on nice. However, the instructor should really consider to update their material to python 3 + turicreate. Python 2 is reaching EOL in 2020 and should be avoided for teaching/training. I did most of my notebooks with python 3 and turicreate, it is really worth the effort to update the material. The tests are ok, but some looked somewhat buggy (as reported in the forum by many users) and could use a revision

Decent course with some good challenges, I would have rated it higher if it was tailored to more used packages (e.g. scikit learn) because even though there was an option to submit using other packages, i would have preferred it if these were in the primary jupyter-notebooks.

I think sometimes instructor jump to some concept without explaining why