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When I first got started in machine learning I implemented algorithms by hand. It was really slow going.
I was a terrible programmer at the time. I was trying to figure out the algorithms from books, how to use them on problems and how to write code – all at the same time. This was the biggest mistake I made when getting started. It made everything 3-times harder and killed my motivation.
A friend of mine suggested I look at Weka. I resisted. I was a student and a C-programmer and I didn’t want to get into Java.
Later, I was looking into decision tree algorithms and I learned that Weka had an implementation of C4.5 (a really powerful method).
I downloaded Weka and discovered that in addition to the Java API, Weka had a fully interactive graphical interface for loading data, running algorithms and reviewing results. Basically, all of the things I was trying to figure out how to do and implement myself, but in a GUI.
Discover how to prepare data, fit models, and evaluate their predictions, all without writing a line of code in my new book, with 18 step-by-step tutorials and 3 projects with Weka.
I was hooked. I started using it for class work and for my own experiments. Later in grad school, I started my own research by writing 3rd-party plugins for Weka (LVQ algorithm and others).
I now recommend it to programmers just getting started because it’s so quick to get meaningful results on a dataset. It also instills best practices like repeatable experiments and statistical methods for comparing results.
If you haven’t already, take a look at Weka.
I have a short tutorial in which you discover how to run a classifier in 5 minutes.
Good luck machine learning!