There are three kinds of lies: lies, damned lies, and statistics.

- Mark Twain

This book is an engaging read full of facts delivered in a really witty manner. It talks about ways in which statistics can be misinterpreted, both intentionally and also as a result of intentional design. *Average numbers* mentioned by surveys, poll results are a tool that can be used for chicanery if their significance isn’t mentioned. Is it mean, median, mode, or something else? A computed statistic associated with a topic is meaningless if the proper context of the phenomenon is not discussed.

For example, suppose…

This term is the cause of more confusion to humans than Einstein’s photoelectric effect. The term “*data”* is so so so often used these days that it seems to overwhelm the general audience, and perplex even the nerds. Anything to do with data has an implicit reference to math, programming, smart people, and a big fat paycheck. But in this data age, can everyone really become Data Scientist?

No. But is everyone surrounded by oceans of data?

Yes. So, does “*data”, “big data”, “data science*” impact our lives?

Very definitely yes. It's a fact, simply look around your room to…

A really fresh-take on AI's progress in a new field. I loved reading this article as it brings an entirely opposing perspective as to mine. Being a skeptic about the "goodness of AI for human race/economy", learning about new advancements only confirms my beliefs. Having come across this article, I observed how it can be used to fill the voids in art, instead of mimicking human-level skills to eventually surpass it.

In a similar vein, we can use AI to gain more insights that are in our blind spots rather than only improving models to get beat the collective human knowledge. Or maybe not. I am a skeptic.

It’s an old saying that practice makes perfect. A wise soul who realized that iterations yield improvement might have concluded that a lot of improvements are perceived as perfection and ended up with that adage. Why is it that despite the truth is out there, not everyone can grab this fruit? Do they not desire it? Do they not want it that bad? Or is it the case that not everyone deserves their “golden apple”?

I do not have a ready-to-eat answer. I have aimed to be extraordinary for as long as my memory serves me. And that desire is…

No seriously. A person who aims high, and wants to build a working science project with molten lava erupting perfectly like liquid chocolate oozing out of marbled brownie as their first summer school project with clay, sand, and fuming sulphuric acid is what an average perfectionist looks like. Or maybe finish the mathematics assignment optimally in the shortest way possible or not complete their math homework because they would either be at the top or not run the race at all. There is a general sense of reaching the rooftop and going beyond, even though the agent might be unaware…

What is linear regression? It is a predictive modeling technique that finds a relationship between independent variable(s) and dependent variable(s) (which is a continuous variable). The* independent variable*(*iv)*’s can be *categorical*(e.g. US, UK, 0/1) or *continuous*(1729, 3.141 etc), while *dependent variable(dv)*s are continuous. Underlying function mapping *iv*’s and *dv*’s can be linear, quadratic, polynomial or other non-linear functions(like sigmoid function in logistic regression), but this article is on linear technique.

Regression techniques are heavily used in making real estate price prediction, financial forecasting, predicting traffic arrival time (ETA).

*Brief*: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to sophistication. In this article, I will be giving a birds-eye view, mathematics(*ba*ye*s*ic maths, nothing ab*normal*), python implementation from scratch and also using sklearn library.

It would be a good idea to check out my blog on K-Means clustering(3 min read) to get a basic idea of clustering, unsupervised learning and the K-means technique. In clustering, given an unlabelled dataset ** X**, we wish to group the samples into

*Brief*: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to sophistication. In this article, I will be giving a birds-eye view, mathematics(*ba*ye*s*ic maths, nothing ab*normal*), python implementation from scratch and also using sklearn library.

It would be a good idea to check out my blog on K-Means clustering(3 min read) to get a basic idea of clustering, unsupervised learning and the K-means technique. In clustering, given an unlabelled dataset ** X**, we wish to group the samples into

*Brief*: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to sophistication. In this article, I will be giving a birds-eye view, mathematics(*ba*ye*s*ic maths, nothing ab*normal*), python implementation from scratch and also using sklearn library.

It would be a good idea to check out my blog on K-Means clustering(3 min read) to get a basic idea of clustering, unsupervised learning and the K-means technique. In clustering, given an unlabelled dataset ** X**, we wish to group the samples into

*Brief*: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to sophistication. In this article, I will be giving a birds-eye view, mathematics(*ba*ye*s*ic maths, nothing ab*normal*), python implementation from scratch and also using sklearn library.

** X**, we wish to group the samples into

MS, UMD | Amateur Writer | Mindfulness | https://www.linkedin.com/in/ribhu-nirek/