Although not as much of a buzzword as “Big Data”, “Machine Learning” is definitely en vogue. To be honest, the phrase sounds much more exotic than the actual technique! Nonetheless, the area of artificial intelligence interests me so I decided to take this class in order to learn more about the use cases of machine learning algorithms. Unfortunately, I finished the class feeling like I had not learned much at all.
Don’t take this the wrong way–I was certainly exposed to a lot of material. In fact, perhaps the best aspect of this class was the breadth of the course content. Many major algorithms are explored and several fundamentals of machine learning are covered. However, I found it very difficult to retain this information and was forgetting even simple concepts from week to week.
It can be difficult to engage students via an online video, but sadly this class felt like it wasn’t even trying. The segments of the videos where the professor is visible look and sound like they were recorded in a closet using a webcam from the early 2000s. The rest of the videos are essentially powerpoint presentations with some annotations. I should mention that this course is taught by the founder of Coursera, Andrew Ng. Therefore, I would have thought that Coursera would want to make the Machine Learning class a “flagship” class for the website. Instead it seems like this was likely one of the first Coursera classes and no one has thought to go back and revise the content.
The assignments for the class consisted almost entirely of algorithm implementation. While this is likely a philosophical decision of the course designers, I don’t really see the benefit. When I work with machine learning algorithms such as k-means clustering, I have no reason to think I can write a better implementation than professionals can. In almost all cases, it’s better to use a library for complex algorithms than it is to roll your own. I would have preferred the assignments to focus more on using the algorithms instead of implementing them.
By design, MOOCs require students to be more self-motivated and dedicated than traditional classrooms. I’m sure that if I had put in more effort into the quizzes and homework assignments, I would have retained more information. However, the quality of the instructional videos and the uninspired content made taking Machine Learning feel like a chore. While I like to see myself as a lifelong learner, this class gave me a bad case of senioritis: “How long until I graduate from lifelong learning?”