Is Artificial Intelligence (AI) the new elixir of all modern problems? Or is it a double-edged sword, sometimes destructive and at other times life-saving?
Data Science Simplified Part 12: Resampling Methods
How does various resampling methods work?
New Retail: How Big Data and AI is Fuelling Retail Transformation
Evolution of Retail Like any industry, retail has gone through its share of transformations. Now the exciting thing about retail is that its development has been symbiotic to consumer behavior. It had to adapt rapidly based on how consumer behavior changed from time to time. For example, between 1900-1940s, cars were not mainstream; the retail […]
An Executive Primer to Deep Learning
Circa 1997, the reigning world chess champion Garry Kasparov was against an unknown opponent. The opponent was formidable. Garry was not playing a human. He was playing the game with IBM’s behemoth supercomputer, Deep Blue. Garry had beaten the opponent in the last few games. However, the game played on 11th May 1997 game was […]
TOP TRENDS IN ARTIFICIAL INTELLIGENCE IN 2018
According to Gartner’s hype cycle of emerging technologies, 2017; Deep Learning and Machine Learning have reached the peak of inflated expectations. Artificial General Intelligence (AGI) and Deep Reinforcement Learning are in the phase of innovation trigger. I have published this article on Analytics Insight that looks into the top trends in Artificial Intelligence in 2018. Happy […]
Data Science Simplified Part 11: Logistic Regression
In the last blog post of this series, we discussed classifiers. The categories of classifiers and how they are evaluated were discussed. We have also discussed regression models in depth. In this post, we dwell a little deeper in how regression models can be used for classification tasks. Logistic Regression is a widely used regression […]
Data Science Simplified Part 10: An Introduction to Classification Models
Data Science Simplified Part 10: An Introduction to Classification Models
Data Science Simplified Part 9: Interactions and Limitations of Regression Models
In the last few blog posts of this series discussed regression models at length. Fernando has built a multivariate regression model. The model takes the following shape: price = -55089.98 + 87.34 engineSize + 60.93 horse power + 770.42 width The model predicts or estimates price (target) as a function of engine size, horse power, […]
Data Science Simplified Part 8: Qualitative Variables in Regression Models
The last few blog posts of this series discussed regression models. Fernando has selected the best model. He has built a multivariate regression model. The model takes the following shape: price = -55089.98 + 87.34 engineSize + 60.93 horse power + 770.42 width The model predicts or estimates price (target) as a function of engine […]
Data Science Simplified Part 7: Log-Log Regression Models
In the last few blog posts of this series, we discussed simple linear regression model. We discussed multivariate regression model and methods for selecting the right model. In this article will address that question. This article will elaborate about Log-Log regression models.
