I signed up for this course and began the reading and videos, but once it was time to begin interacting with the technology required (Amazon Web Services) , it appears this course is not longer supported by the instructors. Most communication on the course seems to have stopped between 1 and 2 years ago. Recent comments on the discussion forum no longer receive a response.

The Lecturers are very enthusiastic, but I was hoping for examples and assignments based on Pandas and Skikit-Learn. Instead the course examples and assignments are based on a machine learning package called Graphlab, that stopped working when it was upgraded to version 2 (there are workarounds that enable it to work locally, but clearly it isn't "enterprise ready")

I feel like it should have been mentioned a lot clearer before starting that this was simply a course in how to use the creators library. These skills are not transferable anywhere else as I would have to pay to use them in future! Would have been a lot more useful as a how to for sci-kit and pandas.

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.

I am a professional SW developer (Embedded C for control units). I do not recommend this course for people who already know something about machine learning. If you want to learn the basics of ML, Stanford's Machine Learning course is a far better choice (is based on Matlab though).

This one here has far too little content.

Moreover, in case you cannot install the needed GraphLab/TuriCreate SW package (only MacOS or Unix, for Windows not always working, as for me also!) then you're basically left alone with finding a) the SW packages you need (I took scikit, numpy, pandas) and the corresponding commands (because the entire course explains ONLY commands for Graphlab, NOT for the other packages) - this is BIG extra work you need to do on your own. Now the big joke is that all other courses in the specialization are NOT based on Graphlab, but on the other packages I mentioned ;).

In addition: Literally 0 support from teachers/mentors in the forum during the course. The students have/had to handle most/all problems themselves. This is a no-go.

Out of date. Should be retired or updated.

This specialization is delayed for months now - very annoying! Don't give them money!

i cannot spend $4000 per year package just to learn this course. sorry i am off...

Unsufficient information for the programming assignments.

Not happy about course 5 & 6 got cancelled.

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

I am so disappointed with this class. I really looked forward to a case study approach that was hands-on. But the instructors have left UWashington (they are both at Stanford now), the full curriculum was never completed, and nobody has updated the class despite the software being radically updated and rearranged after lectures were filmed. Hence, while the teaching is pretty clear, there is no way for the student to follow along on his/her own. UWashington tried to update the Jupyter Notebook pages to try to show how things work with the new software, but those updates were inadequate to enable a student to pass the quizzes. I was never able to get past quiz 3, and had to guess at some of the questions in quiz 2 because the software was not able to provide the information needed to answer quiz questions.

Further, if you are using a Windows machine, the software doesn't work, unless you run Linux in a Docker app or run it in WSL. There, of course, is absolutely NO help from the instruction material on how to do this, and only by digging in the forums can one get some ideas about how to get it working. THIS IS REALLY UNACCEPTABLE for a course from a reputable university that I'm paying for.

DO NOT TAKE THIS COURSE!

The course desperately needs to be filmed again and instructions updated. The lectures are great, but the video part of actually building software to do what the lectures teach is in desperate need of updating to match the current state of the software.

And in case you think that this review is coming from someone who doesn't have a clue...

I am a retired programmer, having worked on advanced simulation and AI software for R&D groups in both Lockheed Martin and IBM for 40 years. I know my way around Macs, Windows, and Linux OSs. While my python skills are not to the level of my C and C++ skills, I have been actively writing python code for the past 6 months. I.e., I know how to program (well) and am (usually) smart enough to work around obstacles and poor documentation.

The course has a fundamental problem, it relies completely on a library developed by one of the instructors, which is not open source. In the end, it seems like a big opportunity of delivering a marketing campaign by the instructors then otherwise.

I definitely will not spend time and money on the other courses of this specialization.

Not possible to complete the course as it relies entirely on a custom software package called turicreate which only runs on deprecated versions of Python that cannot be installed on a modern machine, unless compiled from source.

It's a serious shame as I was looking forward to the style of teaching. Should have used pandas.

The course could be great, if it won´t depend ob Python 2.7 and graphlabs (because scikit isn´t scalable). Also some quiz questions are so hard, that it is impossible to answer only with the material. So they use forum posts to answer how you can find a solution to the quizzez. So in total more a waste of time.

No Open Source Libraries, this course is not educational; it is a sales pitch to use their expensive software. Good luck having an employer pay this amount of money for software when they can hire employees that can use free open source libraries.

The fact that the class uses GraphLab instead of pandas/numpy/sklearn should have been stated up front

The course felt like an advertisement for the professor's toolkit

It was very disappointing that the equivalent standard workflow was not supported

The instructors need to specify that you can run this course specialization using MAC or Linux only. I have wasted my time for the past 3 weeks trying to figure out how to run the Sframe or Turi using windows and could not find any solution.

The instructions to download GraphLab don't work and even when you sign to use the AWS platform the instructions are also old and I haven't been able to start any of the assignments because of that! I want MY MONEY BACK!!!

Shame that it was not possible to progress with this course without using graphlab which the creator of this course himself created. Please see the course as just a training sales promotion for his ML application.

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

Very interesting course, many thanks to Emily and Carlos.

The approach in explaining materials was exactly what I was looking for in order to understand both applications and implementation of AI.

在上這門課之前，其實我就具備了這堂課大多數內容所需的知識，包含這些模型的方法以及數學證明等，因此這門課對我的幫助在於graphlab的使用、各種案例的探討及實踐。

由於有一些先備知識，這門課程的部分案例及題目，是我覺得不太能接受的，例如說：雖然課程中有提到overfitting觀念，但很多題目看起來都只在表達參數越多效果越好。

另外可能是在下才疏學淺搞錯了，在一些linear regression或是logistic regression的範例中，由於案例中的dummy variable過多，造成變數之間線性相依(n維空間中有k組向量，若k > n，必然存在若干向量彼此線性相依)，直覺上有無數組解都可以達到幾近0的SSE，因此縱使結果再漂亮，對那幾個case中的參數，個人其實感到相當的疑惑。類似的困惑還有推薦系統的上課實例等。

課程主要專注在案例分享及各種方法的簡介，整體順序安排相當不錯，兩位講師的描述也相當生動有趣，有很多地方讓人感到耳目一新、獲益良多。不過關於模型的限制覺得還需要更多的解釋，才不會讓人誤用了一些不恰當的方法。

the course concepts were good but as everyone is saying the materials are outdated and you use TuriCreate instead of GraphLab so you have to search for the appropriates functions some times, and the installation was hard too because TuriCreate works only on Linux or WSL, I almost quit the course because I couldn't install it at first

This was a really nice course, clear and introductory, a nice way to get introduced Machine Learning. I did this course cause I had free from my university, but I wouldn't recommend paying for it. The course is abandoned and a little outdated, so don't expect more than just an introduction