Implementing Machine Learning Algorithms in Octave


Experimenting With ML in Octave

Before moving on to how to use machine learning (ML) to solve classification problems (sometimes also called selection problems), let’s take some time to implement one of the algorithms we reviewed previously.

We’ll be using GNU Octave to implement the various algorithms we discuss in this blog. Octave is a great language for prototyping and experimenting with ML algorithms, as it has built-in support for numerical linear algebra such as matrix and vector calculations. This support allows us to concentrate more on implementing our algorithms and not worry so much about “plumbing.” And Octave is interactive, so we can see what’s happening right away and build on results. Once we complete our modeling in a tool such as Octave, we can then implement our refined model in a machine learning platform such as Microsoft AzureML.

GNU Octave runs on all desktop platforms (Windows, MacOS, Linux), and can be downloaded here: After installing Octave, it would be a good idea to go through some of the tutorials here to gain some familiarity with Octave before continuing with this blog post. Note that GNU Octave is compatible with the commercially available MATLAB system, so if you are already familiar with MATLAB, you’ll feel at home with Octave.




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