Machine Learning Foundations: A Case Study Approach

I had already completed Andrew Ng's Machine Learning course (Coursera/Stanford), and a couple of courses in the Data Science specialization (Coursera/Johns Hopkins). Although I loved Andrew Ng's course, I was looking for something more in-depth and a little more useful in my daily work than Octave or R, which are the languages used in these other Coursera courses. So when I saw this University of Washington specialization and read that they use Python, I was very excited.

I was a little less excited when I saw that the courses were not free like the other Coursera courses I had previously taken - but hey, it costs money to produce a course, so paying a little seems reasonable.

However, I soon realized that you don't actually program anything in python, you just slightly modify pre-chewed bits of code. And you don't really do much that is useful in a real job situation because everything is done via very high-level calls to a proprietary (and VERY expensive) python library called GraphLab. They "kindly" give you a one-year free license to the thing, but when you discover that one of the instructors is the co-founder of the company that produce and sell GraphLab, you realize that this is not really a course, it's an info-commercial that you had to pay for.

Don't waste your time or money on this course. Try Andrew Ng's course (on Coursera) or Sebastian Thrun's courses (on Udacity). They are much better and they are free.

Machine Learning Foundations: A Case Study Approach is a 6-week introductory machine learning course offered by the University of Washington on Coursera. It is the first course in a 5-part Machine Learning specialization. The course provides a broad overview of key areas in machine learning, including regression, classification, clustering , recommender systems and deep learning, using short programming case studies as examples. The course assumes basic Python programming skills and it uses a software package called GraphLab that requires a 64-bit operating system running Python 2.7. Grades are based on periodic comprehension quizzes and short programming assignments.

The course covers a broad range of machine learning topics at a high level with the promise of drilling down into the details in future courses in the specialization. The lecturers have good chemistry, but they tend to get distracted when they are on screen together. The video and slide quality are very good and although the delivery is a little rough around the edges at times, the lectures are informative. The machine learning methods covered aren’t necessarily treated as complete black boxes, but the course intentionally avoids getting too deep into the details, putting the emphasis on conceptual understanding.

The weekly labs are contained in short IPython Notebooks—interactive text and code documents rendered in a web browser—that illustrate some simple models in GraphLab. The labs themselves are easy and don’t require much coding other than calling various built in GraphLab functions. The hardest part about the class is getting your programming environment set up in the first place. If you don’t have a new version of 64-bit Python 2.7, you can’t run GraphLab. It is relatively easy to get set up if you can use the recommended Anaconda Python distribution, but getting things set up manually on an existing Python installation may prove troublesome. The instructors provided some workarounds for doing the course without GraphLab or using GraphLab on Amazon’s cloud computing service; I wouldn’t take the course without getting GraphLab working in some form. Many students decried the use of a non-open source package for an open class; I think it is useful to be exposed to new tools and GraphLab seems cleaner than Python’s popular scikit-learn package. In this sort of course, the focus should be one concepts rather than syntax.

Machine Learning Foundations: A Case Study Approach achieves its goal of introducing machine learning at a high level without rushing or trying to cram too much into any particular week. What the professors lack in terms of polish they make up for with enthusiasm. Compatibility and setup issues will be a roadblock for some, but overcoming them is worth it.

I give Machine Learning Foundations: A Case Study Approach 4.5 out of 5 stars: Great.

This course is easily the best introductory course to Machine Learning one can get. Well-designed, beginner-friendly but also rich in content at the same time. There's a pretty nice balance between theory and practice. Basic, foundational machine learning techniques are taught via GraphLab, a Python-based software specialized in analytics. The course instructor is also one of the founders of this great software.

If you are looking for some courses that are more difficult and challenging than Andrew Ng's Machine Learning course, this course is not for you.

We should understand that, this course is designed to hold learners' hands and walk them through understanding the very basic foundation of machine learning. With that objective in mind, this course did really well in familiarizing a complete beginner with all the terminologies and techniques in Machine learning: regression, classification, clustering, etc.

For people who look for complex algorithms and deep understanding of techniques, this course is not meant for them. However, as the instructors promise, the next courses in the specialization will delve deeper into those complex topics. Hence, there's a great deal of excitement to look into this specialization.

So, the theory part has been really beginner-friendly. For the practical, programming part, it's also very digestible. Remember, the target of this course is beginners, so if you're some expert Python user looking to learn some magics. you will be disappointed as there is not much advanced Python techniques here. However, there's some Python basic requirement, as the software they use is based on Python and you will need to know some basic data structures and iterators in Python to have it easy.

About the software Graphlab, some people complain that the course is trying to sell GraphLab. It is not doing so, and it is unfair to label the course as having such a perverse intention. The course instructor made clear that GraphLab is available for free, indefinitely, for academic purpose. Commercial use is forbidden by the license, which is obvious, and reasonable.

Looking forward to the next courses in this specialization, where the instructors promise that the concepts will be more challenging and complex but that also comes with the possibility of building exciting machine learning applications.

EDIT: After finishing 4 courses in the specialization, my opinion has gone downhill. Here is my review of the whole specialization: https://www.class-central.com/certificate/machine-learning-specialization#reviews :END EDIT

I completed this course in mid-February. After the Andrew Ng course from Stanford/Coursera, it was relatively easy but I was happy to get a simple Python-based introduction to machine learning since Andrew Ng's course uses Matlab/Octave.

Compared to the big (free) MOOCs, it is a little disappointing to see so little activity in the forums. The approx. $75 price tag keeps enrollment down and I didn't manage to have a lot of interesting communication in the forums (compared to the bigger MOOCs).

I installed Graphlab on my machine and would NOT recommend trying this course unless you plan to use Graphlab. Seems like the folks who are trying to use scikit-learn for the course are having trouble getting the right answers.

Overall, I don't regret taking this course but remember that it is part of a 6-course series and the series is not yet complete. At this time there is also the second course in the series (Regression, which I just finished today and found to be somewhat more in-depth). The third course in the series is launching this week (Classification). I plan to take it but I will take a month break first and wait for the initial bugs to be worked out.

BTW: University of Washington does now appear on the Coursera certificate. I think there was some doubt about this earlier, but here is my certificate (on closer inspection, I noticed the name of my professor is "John Doe"...): https://www.coursera.org/account/accomplishments/verify/Z6ZXWN7D8US4

This is supposed to be an introductory course to machine learning. It is not quite introductory in the sense of a gentle start from basics, but is geared towards providing some kind of introduction or overview of regression, classification, clustering, recommendation systems and deep learning.

Half of the course consists of lectures on theory, and the other half consists of lecture-labs in Python using Graphlab. I found 4/5 of the lectures fine, sometimes with a bit of self-research added on - the two instructors, Emily and Carlos, were engaging and had good chemistry between them. The remaining 1/5 I did not understand despite trying to do self-research on the topic or issue.

In some cases the quiz questions and lab-assignment instructions were utterly poorly written. Some were downright confusing. Some just did not make sense. Some lab-assignments required students to make use of Graphlab commands not taught during the lecture-labs. Some quiz questions covered topics not at all covered during the lectures. There was a lot of frustration with these issues, some expressed as early as 7 months ago, but the material does not appear to have been updated to resolve them.

Unfortunately, despite having 2 instructors, 3 teaching staff and 29 mentors, most students who were facing technical issues with the datasets or Graphlab commands, or who did not understand the lectures or quiz questions, received no help from the instructions TS and mentors. There was not very much participation from them, and I never one post from the instructors and TS.

I have checked out Andrew Ng's and Udacity's ML courses, and preferred UW's out of the three. However the course materials need to be re-examined and improved on.

This course is pretty basic, offering an overview, and some workshops to work through.

I gave this course a low 2 star rating because the workshops use software called Graphlab. Graphlab is actually pretty cool, and is made by one of the course presenter's startups. However, Graphlab is not available for commercial use. Before Apple acquired Turi, the company that makes Graphlab, I've heard it was available at a special price of $400/month, but even that is no longer available.

I started and got a long way through this course with the intent of learning software that I can use in my daily work, only to find out that would be impossible. Frustrating waste of time.

(I'd already done Andrew Ng's Stanford course previously - which was great for learning the theory)