Data science/ML/AI

The Simplest Machine Learning Cheatsheet

Kafka 101

A Stanford CS' Lecture note diving into supervised/unsupervised algorithms, neural networks, SVMs with math proofs and Python pseudocode.

AI vs Machine Learning vs Deep Learning Vs Generative AI

4 Pillars of Data Science

Notes on HDFS, MapReduce, YARN, Hadoop vs. traditional systems and much more... from Columbia University.

Eigenvalues & Eigenvectors — Why PCA Actually Works

You’ve heard of PCA. But what’s really happening underneath?

PCA finds the directions (vectors) where your data varies the most.

Those directions are eigenvectors of the covariance matrix and the eigenvalues tell you how much variance each captures.

You’re basically rotating your data to find its “natural axes.”

PCA isn’t compression — it’s discovering how your data wants to be seen.

Covariance vs. Correlation: Same Family, Different Story

People use them interchangeably but they measure different things.

Covariance tells you the direction of relationship (positive or negative).
Correlation goes further; it tells you the strength, normalized between -1 and 1.

So while covariance can be 2345.67, correlation says 0.92. clear, interpretable, scale-free.

Covariance shows movement, correlation shows consistency.

K-Means Clustering

Top 6 Data Concepts

Covers basics of Linear Regression for modeling numerical data, including assumptions and applications in genetics, from University of Washington.

Regression Analysis Cheatsheet

This is our latest post from Instagram, saved as PDF.

It's a comprehensive breakdown(as always) explaining the difference between Relational DB and Graph DB in a fun and easy to grasp way.

⚠️ Spoiler alert: You will love it!

Here's our Instagram post: Relational DB Vs Graph DB