So here are my three principle experiences you won’t effectively discover in books. 1. Evaluation Is Key The main goal in data analysis/machine learning/data scienc...
The following problems appeared in the exercises in the Coursera course Image Processing (by Northwestern University). The following descriptions of the problems are ta...
This problem appeared as an assignment in the coursera course Natural Language Processing (by Stanford) in 2012. The following description of the problem is taken direct...
Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, wh...
This article was written by Koustuch on CV-Tricks. In this series of post, we shall learn the algorithm for image segmentation and implementation of the same using Ten...
Once dubbed as the sexiest job of the 21st century by The Harvard Business Review, data scientists take pride in having adept technical skills in providing solutions to p...
Do you often go with gut feeling rather than data and insights? Is your data stored in separate databases, in different formats with different values? We all have bad ha...
Target corporation’s massively profitable data science project threw them into the news spotlight a few years back. Their story makes for a valuable case study in brid...
Here is a nice summary of traditional machine learning methods, from Mathworks. I also decided to add the following picture below, as it illustrates a method that was ve...
Been trying to pull together a taxonomy of 3D data viz. Biggest difference is I think between allocentric (data moves) and egocentric (you move) viewpoints. The differenc...