Modelling Risk: the absolute and the relative

What is the risk of a new creditor to default on their loan? what is the “risk” of watching a certain movie on Netflix given a certain viewing history? What is my risk of dying given a certain diagnosis and how is this affected by a certain treatment? Risks are all around us, and quantifying these risks is becoming increasingly popular. Providing the right kind of analysis to these key questions is crucial to making the right decisions.

Read More

15 Free Data Science, Machine Learning & Statistics eBooks for 2021

We present a curated list of 15 free eBooks compiled in a single location to close out the year. Among other articles highlighting such materials, I have written a series of posts since the pandemic erupted, in the case that more people spending more time at home may result in more time for reading.

Read More

Simulating the FIFA World Cup 2022

Who does the data choose to win the largest international football tournament yet? The grandest and most exciting of all football tournaments is still a ways off (2022), but in times like these I find solace in the fact that there are better things (like the next World Cup) that are rapidly approaching with every day that passes. The question on everyone’s mind is always: who wins? My mission is to see what the data says.

Read More

A Quick Introduction to Time Series Analysis

In my first article on Time Series, I hope to introduce the basic ideas and definitions required to understand basic Time Series analysis. We will start with the essential and key mathematical definitions, which are required to implement more advanced models. The information will be introduced in a similar manner as it was in a McGill graduate course on the subject, and following the style of the textbook by Brockwell and Davis.

Read More

The last Machine & Deep-Learning Compendium you’ll ever need

In the last 3 years, I have been curating everything related, directly or indirectly, to machine-learning (ML), deep-learning (DL), Statistics, Probability, NLP, NLU, deep-vision, etc. I started curating a compendium because I wanted to expand the scope of my knowledge. I believe that every researcher and data scientist (DS) should strive to learn more on a daily basis, not by hitting task-related walls and solving them, but as a lifelong learning practice.

Read More
1 2