Kursteilnehmer-Bewertung und -Feedback für Machine Learning Foundations: A Case Study Approach von University of Washington

The videos are engaging and the examples very interesting. But They use a library that only works with Python 2 graphlab) and needs some kind of environment not accepted by all laptops. I have 2 computers. On one I was able to install their library but my other noteboooks that are using python3 could not run anymore. It messed up my python environment and I can't get to clean every thing. I tried to install their library on another laptop (HP with slow processor) but the library didn't work. So I decided to use sci-kit instead. The issue is that don't get exactly the same results as they do. Which is an issue for the quizzes (answer for RMSE for example not the same) They should wait for graphlab to be compatible with python3 and to be less demanding in environment setting and to be compatible with normal laptop before offering this class.

The course is good. Very well taught. The issue is with Turicreate. Im not sure why we have to use Turicreate. Looks like its being promoted. Thats ok, but the real issue is installing it. I lost nearly a week on that. To figure out what to do and how to use it. It was so frusterating to start the course like this. But then, I found a solution in the discussion forum by Thet oo Zin, who has converted all the SFrames that can be used in Google Colab. That was helpful.

Then, many commands are in Turicreate. After this course I have to go figure out how those commands are written outside of Turicreate. For example:

1 - Load_Data = turicreate.SFrame('path of dataset')

2 - knn_model = turicreate.nearest_neighbors.create(x, y, z)

But the course is amazing, interesting and easily understandable for a newbee to python/ML like me.

I did pick up some very helpful information which was great, so for that I give it 3 stars. I failed to give it 5 stars because of the use of Graphlab Create and the subpar programming assignments. Apache Spark is a more powerful version of Graphlab Create, it's completely open source, and major companies like Netflix are using it. Carlos (instructor) is the owner of Graph Lab/Dato and uses this course to push and teach his platform. The programming assignments at times feel like he's just trying to teach me Graph Lab instead of the concepts. I'd have no problem with Graph Lab if it was completely open source, but it's not, so it feel like I spent a lot of money to be pitched Graph Lab. Class was not a complete waste, but I'd like it a whole lot better if they used Spark or open sourced Graph Lab.

I am giving this course 3 stars for a few reasons:

1) (Negative) Essentially no instructions were given for how to get Graphlab to actually work in Python outside of the notebook. I already have python on my computer, but the course basically only explains ipython notebook.

2) (Negative) I think the course would be a lot better if it didn't use this pretty graphical interface of ipython notebook. Why use this? I feel like this was done to dumb things down so that more people with no programming knowledge could get by. In reality it just makes everyone learn less. Using python normally, with graphlab imported, would be much better.

3) (Positive) The lectures on things other than ipython notebook were fairly good, and I like how the specialty is structured with case studies.

This course is a good intuitive-level introduction to machine learning. The presentation of the materials by the instructors is crystal clear and pretty much perfect. However, if you are looking (like I was) for a more in-depth course on machine learning, having already taken an applied-level machine learning course, skip this course and go straight for the next one in thsi specialization!

Big drawback also is the instructors reliance on GraphLab and related libraries. It is not commonly used and not really supported (for one, no Python 3 support!). I would strongly recommend making the required datasets for this course available in formats that accessible by libraries that are *far* more commonly used in ML applications like Pandas or Scikit-Learn

The course gives a nice overview of machine learning but does not go in depth. Of course this will be done in the following specialization but the pace might have been set higher to my taste. I also had a lot of trouble getting the software to run. As a matter of fact, the python package used (graphlab) uses outdated SFrame package which has changed name. FUrthermore, you cannot get the notebook running if you have installed anaconda3. A good thing about using graphlab is that it hides all the implementation away from the user so you can really play with the algorithms without getting to confused. A drawback is that this makes it harder to translate the knowledge to my own job where I do not have graphlab available.

Recently, I got a certification of Machine Learning course of Anderw Ng.

So the first course of Machine Learning Specialization is too easy for me.

But I think it's not a matter of how easy it is.

This program poorly explain how algorithms work

Even if the lecturers keep saying that we are going to study in detail in the later courses,

it's very difficult to stand boring situations.

And there's a serious problem.

They provide data for programming assignments, which shows different results compared to the one in the video lectures. So I am soooooooooo confused.

There are some small hardships more. But I am stopping writing this.

If at least one of the lecturers find my review, please contact me.

T​he good part is the very good and simple introduction of ML concepts, which are assesed during quizes. It makes you thinking and understanding the concepts of ML.

The less good part - t​he course should be updated in such way that video material fit the assignments. The usage of graphlab during presentations and turi create during assignments is not a good way to ensure complete understanding of materials.

T​he recording conditions were poor sometime ( police sirene or landing plane can be heard ). Some URL links on readings before quizes are not working and one assignment still has wrong answer considered as correct even the students posted clear messages toward mentors months ago.

The course is completely an introduction to Machine learning and It gives you the very basics of machine learning but not in details of course! Otherwise there was no need for splitting it to 5 courses which they have canceled 2 of them. The concept parts of each week are great but unfortunately the problem is Graphlab, which you will have problems installing it on a windows and the library itself is old-fashioned and no one use it because the updated version is called Turicreate and you need to seek the docs to keep up with course in Turicreate. So i think you will be disappointed from coding parts but concepts presentations are good and gives you a nice insight,

Lectures are very easy and unnecessarily long and slow. I had to watch all of them on x2 playback speed to not die of boredom. Also, what is the point of breaking them into 3 minute chunks? Some people may need more time just for getting to the right mood to learn. I cannot imagine anyone watching 3 minute video, doing something else and returning back to it. Also, it requires me to start the next video and set the speed to x2 again.

Overall, lectures are OK and material is explained well.

Programming assignments are worthless, as they are basically "Fill one line of code that does X. By the way, here is the syntax. And here is the data to use." No thinking required.

The concept and overall material covered was exciting. However, the lessons often did not connect to what was actually being tested. This course requires a lot of reading of the Dato SFrame manual. If the instructors focused more on showing how to actually do some of the tested material, I would've given this course four to five stars. At the end of the day, this course does give an entry level data scientist such as myself the ability to do some 'cool' analysis, which I truly appreciated. Overall, I would recommend this course to a fellow data scientist. I just hope the instructors focus on teaching content with more focus and clarity.

Its a fine course but most of the coding comes from the program Graph Lab, which is only free for academic purposes. So you won't be able to take your skills outside this course unless you 1) do all the HW assignments in an open-source and struggle (because there is no assistance for this method) or 2) you pay for GraphLab once you are done with the course (not worth is with all the open source packages out there). The instructors also don't make it easy for users to use the open source packages because Graph Lab splits the data differently than these other sources, making our answers always slightly off.

The video lectures provide a clear and concise introduction to interesting topics in machine learning (ML). However, the exercises are very general and use 'black box' ML algorithms for most of the solutions. For me, the exercise structure was more confusing than educating. I am aware that this is the intro course to the specialization, and I am looking forward to actually building the algorithms in the future courses. Too bad you can only take the entire specialization over the course of ~6 months, and not at your own pace! Especially since the homework is checked automatically.

This is superb introduction to Machine Learning. I've tried to read the "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" but can't understand the ideas in the book until I've finished this course. Overall, this is a great start for those who want to learn Machine Learning concept.

The downside is it uses non-standard Turicreate rather than popular Sci-kit Learn, or Tensorflow. But as they said it is more easy for a beginner to grasp the concept using Turicreate than sci-kit learn which is true.

The course gave a great overview of Machine Learning through case study and will help me a lot I think to design similar courses in the future. On the bad side, I have noticed the course was developed some five years ago and that the videos were not updated. The fact that for instance Graphlab changed to TuriCreate is annoying since we have videos and the notebook does not correspond to it. Furthermore, The mentors are not responsive enough on the forum. I have an unanswered question and noticed many other questions were left with no answers.

I really liked the fact that we were given an overview of all the machine learning techniques before we actually delve deeper. However I would have rather appreciated it further if we used open source python libraries rather than graphlab!

I think the use of graphlab really did limit our scalability and use elsewhere other than on the course.

Please do consider using open source tools in further courses and also provide starter code for the assignments in one open source library in addition to the code provided using graphlab.

Although the concepts presented in the course are interesting, all course examples are based on a proprietory python library (Graphlab) which you are most likely will never use in real life. As the course suggests you could use open source libraries (scikit for ex.) but since all examples do not use it, it will take 2-3 times more time to figure out how to do the same assignment using open source libraries. So if you hope to learn ML concepts applied to scikit, pandas, etc. that's probably not the best course for it.

Very frustrating. This course is a good Machine Learning overview, and light on programming. BUT the homework is based around an opensource library, TuriCreate - this is only available for Mac OS. Windows users will have a harder time with this course.

The workaround I found was to register for a student version of GraphLab (which the course previously used). I used an older version of Anaconda that I got from the GraphLab website, and modified the homework assignments to use GraphLab instead of TuriCreate

Nice overview of the specialization. Since it aims at showing the advanced and interesting things you will learn during the specialization, some of the practical sessions are way too advanced. Thus, for me felt more like a mechanical copying of what the instructors did rather than an actual assessment of what I understood. Also, since some of the applications are actually repeated at the beginning of the main courses, it feels like a repetition somehow when then you move to the specialized courses.

I really enjoyed the case study approach that's why the 3 stars but I'm not gibing it a 5 because some of the videos could just be skipped because half of them are the instructors laughing and the other half is some important info. Also it looks like they don't really care about the community because not all questions asked in the forums get answers. Finally, there are some clear mistakes in the Quizzes that haven't been resolved although many people have complained in the forums.

The course could have been marked by 5 stars if it weren't for the promotion of a commercial Python library developed by one of the speakers. There is no way a student could complete the course without having Python installed and a free licence acquired from dato.com.

Students should be able to use any programming languages and scientific libraries to do their homework and the subsequent courses of the "Machine Learning" specialisation are excellent examples of such approach.

I was unable to get graphlab running – had to use turicreate instead. Also, the most interesting part, deep features, came a bit "ex machina" – without a proper explanation how to create what was prepared. Also, I really miss the parts 5-6 of the specialization which look very interesting. The basics are already well covered at many places. If the parts 5-6 were existent, I would probably take the whole specialization. This way, I will pass.

I enjoyed the course and I will continue with the specialization. I am giving a 3-star rating as i) the lectures need to be updated with correct data or need to provide guidance as to when one should expect individual difference when following along with the notebook, ii) instructor / mentor response in the discussion forums is lacking, iii) graphlab is now an outdated tool as it is not commercially available.

It would be better for the course to focus on using scikit-learn for machine learning. The course focuses on using GraphLab (https://turi.com/download/academic.html), which is a commericial product, free for academic use. I'm doing this course for professional purposes and my preference is to gain familiarity with free/open source solutions that I will be later able to utilise in production environment.

To be honest, this course is not friendly to windows 10 users because it forces students to use the apple Inc's Turicreate instead of the most popular sklearn. Admittedly, windows 10 users can still install the Turicreate by WSL but not everyone wants to add a subsystem to their windows just for this course. Except for this, this course has a nice structure and the content is really practice-oriented.