“Proof by example” and “slothfull induction” can “prove” to be very dangerous.

Hasty generalization has always plagued the scientific realms for centuries. Often when the stakes are high, a rational argument for justification becomes a crucial requirement to minimize the potential loss that a fallacy could cause. These forms of sloppy approaches are observed very frequently in the field of sampling.

“A minimum of 30 observations is sufficient to conduct significant statistics.”

This is open to many interpretations of which the most fallible one is that the sample size of 30 is enough to trust your confidence interval. Sampling is…

If you have ever delved into the world of data science, then it will not be an absurdity for me to assume that by now you have certainly encountered the term Neural Networks somewhere, at some point in your journey towards probing Machine Learning and Artificial Intelligence or data science in general.

Applications of machine learning (ML) are now almost an integral part of our everyday life. From a speech-recognition based virtual assistant in our smartphones to super-intelligent automated drones, ML and artificial intelligence (AI) is revolutionizing the dynamics of human-machine interactions. AI algorithms, especially the convolution neural networks (CNN) have made computer vision extremely powerful than ever. While the applications of it are breathtakingly awesome, it could be very intimidating to build one’s own CNN model, especially for a non-programmer or a beginner in data science, in general. As an R lover, it was not difficult for me to assert that…