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Everything I’ve written on pythonandr.com, newest first — ten years of the maths, worked in code.
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Incident Report: A Risk Quant Reviews DeepLearning.AI’s Agentic AI Course
A model risk quant reviews DeepLearning.AI’s five-module Agentic AI course — module by module, lab by lab — benchmarked against Hugging Face, LangGraph, CrewAI and more. Great ideas, honest teaching, demo-grade code.
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When the Skeptic Reads the Manual: A Risk Quant Learns to Build AI Agents
In which I build the perfect AI-course reading list on DeepLearning.AI, then do to it what I’m paid to do to everyone else’s models — validate it, starting with how old the courses actually are.
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Scraping the Daily India Covid-19 Tracker for CSV Data
Turning a live public dashboard into clean, analysis-ready data — pragmatic data engineering in Python.
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Linear Algebra behind the lm() function in R
What ordinary least squares actually computes — QR decomposition, the normal equations, and why R does it the way it does.
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Installing Tensorflow on Windows is Easy!
Being a loyal Ubuntu user and having avoided using Windows for Tensorflow (or Python, for that matter) , I was pleasantly surprised to find installation to be clean and hassle-free.
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Linear / Logistic Regression in R: Dealing With Unknown Factor Levels in Test Data
Let’s say you have data containing a categorical variable with 50 levels. When you divide the data into train and test sets, chances are you don’t have all 50 levels featuring in your training set. This often happens when you…
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Quick Way of Installing all your old R libraries on a New Device
I recently bought a new laptop and began installing essential software all over again, including R of course! And I wanted all the libraries that I had installed in my previous laptop. Instead of installing libraries one by one all…
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Key Insights on Sberbank Home Price Predicting Kaggle Competition Coming Soon…
This post is more about data science and Kaggle than about R or Python. I am currently taking part in my 2nd Kaggle competition, Sberbank Russian Housing Market — Can you predict realty price fluctuations in Russia’s volatile economy? I’ve…
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Endogenously Detecting Structural Breaks in a Time Series: Implementation in R
The most conventional approach to determine structural breaks in longitudinal data seems to be the Chow Test. From Wikipedia, The Chow test, proposed by econometrician Gregory Chow in 1960, is a test of whether the coefficients in two linear regressions…
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Abu Mostafa’s Machine Learning MOOC – Now on EdX
What is learning? Can a machine learn? How to do it? How to do it well? Take-home lessons.
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MITx: 6.008.1x Computational Probability and Inference
Learn fundamentals of probabilistic analysis and inference. Build computer programs that reason with uncertainty and make predictions. Tackle machine learning problems, from recommending movies to spam filtering to robot navigation.
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Analytics Vidhya Workshop / Hackathon – Experiments with Data
This was a hackathon + workshop conducted by Analytics Vidhya in which I took part and made it to the #1 on the leaderboard. The data set was straight-forward and quite clean with only a minor need for missing value…
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Implementing Undirected Graphs in Python
There are 2 popular ways of representing an undirected graph. Adjacency List Each list describes the set of neighbors of a vertex in the graph. Adjacency Matrix The elements of the matrix indicate whether pairs of vertices are adjacent or…
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Deterministic Selection Algorithm Python Code
Through this post, I’m sharing Python code implementing the median of medians algorithm, an algorithm that resembles quickselect, differing only in the way in which the pivot is chose, i.e, deterministically, instead of at random.
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scikit-learn Linear Regression Example
Here’s a quick example case for implementing one of the simplest of learning algorithms in any machine learning toolbox – Linear Regression. You can download the IPython / Jupyter notebook here so as to play around with the code and try things out…
Anirudh Jayaraman 