Category Archives: Python Programming

Gaussian Mixture Model with Application to Anomaly Detection

There are many flavors of clustering algorithms available to data scientists today. To name just a few would be to list k-means, KNN, LDA, parametric mixture models (e.g. Gaussian Mixture), hidden Markov for time-series and SOMs. Each mentioned model has … Continue reading

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High Performance Python

Greetings All, I have recently come across a nice and short youtube talk given by Ben Lerner on how to improve performance of Python code. Here is a quick rundown of the techniques he mentions which I found particularly useful: … Continue reading

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Two Ways to Perform Linear Regression in Python with Numpy and Scikit-Learn

Greetings, This is a short post to share two ways (there are many more) to perform pain-free linear regression in python. I will use numpy.poly1d and sklearn.linearmodel. Using numpy.polyfit we can fit any data to a specified degree polynomial by … Continue reading

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K-Means Clustering and How it Relates to the Gaussian Mixture Model

Greetings, I would like to write a post on the Gaussian Mixture models. The post would be a tutorial with a, hopefully, intuitive explanation of when and how to use Gaussian Mixture (GM) models. However, before I begin with GM, … Continue reading

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Learning Predictive Rules on the Poker Hand Data Set

Hello again, In my last post I have shared a rather simple python code to build a decision tree classifier to recognize a hand in a poker game. The simplicity of my solution stemmed from the fact that I added … Continue reading

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