Entropy and Information Gain

Entropy Entropy as a concept is rarely mentioned while discussing Machine Learning. However, the concept forms the basis for a lot of the Machine Learning algorithms that we see today. In terms of a formal definition, Entropy $latex H(X) $ of a variable is defined by $latex \sum_{x} p(x)*log_{2} \left ( \frac{1}{p(x)} \right )  $ But …

Structured Prediction using Conditional Random Fields

In this tutorial, I would be explaining the concept of structured learning/prediction and the use of Conditional Random Fields (CRF) for achieving this. Before we start discussing about CRF's, its essential that we understand what structure prediction is and why do we require it. What is Structured Learning? At present, Neural networks and its numerous …

PCA implementation : Iris dataset

We will be taking the Iris dataset to demonstrate how PCA works and how it defines better predictors for the dataset. It's a very common dataset and comes installed in R. The Iris dataset has 4 predictors: 1. Sepal Length 2. Sepal Width 3. Petal Length 4. Petal Width These predictors are used to determine …

Data Transmission using Xbee

I recently had the opportunity to work with XBee's as part of one of my projects. There's a ton of online resources on how to set up XBee's and how to get them running. However most of these resources used Arduino codes which were very specific and which wouldnt work in all cases. In this …