Coursera is offering its Machine Learning course again, beginning March 8, and I highly recommend it. You already know the obvious, that it is a course on an incredibly timely career skill and it is free, but until you take the course you can't know just how good the course really is.

You will learn how to write algorithms to perform linear regression, logistic regression, neural networks, clustering and dimensionality reduction. Throughout the course Professor Ng explains the techniques that are used to prepare data for analysis, why particular techniques are used, and how to determine which techniques are most useful for a particular problem.

In addition to the explanation of what and why, there is an equal amount of explaining how. The 'how' is math, specifically linear algebra. From the first week to the last, Ng clearly explains the mathematical techniques and equations that apply to each problem, how the equations are represented with linear algebra, and how to implement each calculation in Octave or Matlab.

The course has homework. Each week, there is a zip file that contains a number of incomplete matlab files that provide the structure for the problem to be solved, and you need to implement the techniques from the week's lessons. Each assignment includes a submission script that is run from the command line. You submit your solution, and it either congratulates you for getting the right answer, or informs you if your solution was incorrect.

It is possible to view all of the lectures without signing up for the class. Don't do that. Sign up for the class. Actually signing up for the class gives you a schedule to keep to. It also allows you to get your homework checked. When you watch the lectures, you will think you understand the material; until you have done the homework you really don't. As good as the teaching is, the material is still rigorous enough that it will be hard to complete if you are not trying to keep to a schedule. Also, if you complete the course successfully, you will be able to put it on your resume and LinkedIn profile.

You have the time. When I took the class, there was extra time built in to the schedule to allow people who started the course late to stay on pace. Even if you fall behind, the penalty for late submission is low enough that it is possible to complete every assignment late and still get a passing grade in the course.

I am going to take the course again. I want to make review the material. I also want to try to implement the homework solutions in Clojure, in addition to Octave. I will be posting regularly about my progress.

You may also be able to find a study group in your area. I decided to retake the course when I found out that there was going to be a meetup group in my area. Even without a local group, the discussion forums are a great source of help throughout the class. The teaching assistants and your classmates provide a lot of guidance when you need it.

Sounds awesome, but how much math background do you need to keep up with the technical/theoretical side? Would it still make sense if you didn't already know linear algebra?

ReplyDeleteYes, if you understand algebra, you will be able to understand the math.

DeleteI don't think I could have told you before taking the class that Linear Algebra was the official name of matrix arithmetic.

Matrices were something they covered for a week or two every year when I was in school, and I could never remember how to do them from one year to the next. In this class, you will learn that they are not just a funny way to right numbers, but what they represent, and why they are useful.

Really, the trick is learning how to represent inputs and formulas in matrices, and then writing the expressions to calculate them in Octave. It is a bit challenging at first, but quickly you get to the point of realizing that you are just doing algebra more efficiently.