Jedi/Rockstar/Ninja in Development

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Spam classification in Python/NLTK

Today's (very first) session is Machine Learning for Hackers (ML4H), chapter 3.

Machine learning is on the top of my list of skills, and I'm learning it in Python, which has ample libraries and community support and a really clean and easy syntax. ML4H is written for R, but there's already a blog series and GitHub repo porting the code to Python.

I'm already familiar with Naive Bayes classification, the approach used here, but I haven't used the Python-based NLTK library. In fact, I recently rolled my own text classification setup in Python and Scikit-Learn's Naive Bayes, so I'm paying particular attention to how the code is structured and what NLTK provides.

The code uses the following methods:

Python for Data mining and Analytics

On The Learning Curve

Python is probably one of the easiest language to learn and with the strong adoption rate from the programming community, I think it is worth it to invest time in learning python.

My journey with python started with a course of Artificial Intelligence and then due to a search engine development course that used python. But I haven't used Python for about 2 years now.

Now a days, I am more interested in doing Data mining and analytics with Python. So here is the first lesson:

(More on this later)

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