I'm currently taking a class covering some topics in machine learning. The class is taught in MATLAB using Liblinear so far. I was curious though what kind of tools people used in the real world to tackle these problems. As I'm learning now, MATLAB is great to get your feet wet, but it is seriously lacking in terms of performance, scalability, and maintainability. Is it mostly commercial software? Do people rewrite their SVMs from scratch every time (doubt it)?
Also, what language to people use? MATLAB seems to be popular in research circles, but Python seems to be growing in support as well. What do people at say Google or Facebook use? Is it all heavily-optimized closed source code?
Best Answer
MATLAB was primarily developed for optimization and mathematical simulations in engineering problems. But yes it has performance issues when it comes to machine learning, optimization, etc., in terms of customizing ability.
Over time, most statistical analysis / machine learning has shifted to R and Python, because of an active community presence for development of almost any complex algorithm. You don't have to write code for SVM or neural networks from scratch, unless you really want to change to algorithm itself, which is also possible, and which is what Google and Facebook do internally.
So if you want to try machine learning for studying purposes, WEKA, R and Python will do the job. but if your really want to develop some data analytics products based on these algorithms, Python and R are the way to go. R has a steep learning curve, though.
WEKA became popular because earlier in industry, because most of the analytics practitioners came from an IT (Information technology) background and hence were comfortable with JAVA, but over the time mathematicians and computer scientists have moved to R and Python.