Data Cleaning and Wrangling With R
Originally posted by Michael Grogan. One of the big issues when it comes to working with data in any context is the issue of data cleaning and merging of datasets, since...
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Detecting Bias with SHAP
StackOverflow’s annual developer survey concluded earlier this year, and they have graciously published the (anonymized) 2019 results for analysis. They’re a rich vie...
US Arrests: Hierarchical Clustering using DIANA and AGNES
Introduction Data Science and Machine Learning are furtive, they go un-noticed but are present in all ways possible and everywhere. They contribute significantly in all t...
The Mathematics behind Artificial Intelligence and Deep Learning
“The book of nature is written in the language of mathematics!” – Galileo. Each has their own journey through the deep woods of AI & ML. My own expl...
Bayesian Machine Learning Part 5
Bayesian Machine Learning (part -5) Introduction: Expectation-Maximization In this blog we are going to see how Expectation-maximization algorithm works very closely. Thi...
Excel Cheat Sheet
This 12-page document lists hundreds of Excel functions and formulas, covering Lookup/Ref Functions String/Text Functions Date/Time Functions Math/Trig Functions Statis...
Bayesian Machine Learning
Bayesian Machine Learning (part – 1) Introduction As a data scientist, I am curious about knowing different analytical processes from a probabilistic point of view....
Delivering Enterprise Analytics
This article broadly describes the capabilities that constitute an enterprise analytics program or competency. The intention initially, was to provide tips on mitigating ...
3 AI use cases in Agri Tech
Introduction Agriculture is a major segment powering the Asian economy. A small transformation in agricultural outcomes can have a huge impact on 2 dimensions – economi...
Simulated Significance
I pulled out a dusty copy of Thinking Stats by Allen Downey the other day. I highly recommend this terrific little read that teaches statistics with easily understood e...