R² is a widely used measure of fit, but for many analysts, it is just a number. They believe high R² ➡ Good predictions. This is not always true! Now I will clarify. 🔽 R-squared

My copy is finally here!

Conditional probability is the single most important concept in statistics. Why? Because without accounting for prior information, predictive models are useless. Here is what conditional probability is, and why it is essential.

How to build a good understanding of math for machine learning? I get this question a lot, so I decided to make a complete roadmap for you. In essence, three fields make this up: calculus, linear algebra, and probability theory. Let's take a quick look at them!

Four years ago, I started a project that turned out to be life-altering, and it has just passed a huge milestone. I'm happy to announce that my Mathematics of Machine Learning book will soon be published by Packt Publishing! Yes, there will be a physical version.

I share similar tips in my newsletter: Data Ground Up. Link in bio. You can read the full article there and sign up for similar posts.

But when to use it then? 1️⃣ For quick explorations 2️⃣ Using star to reference rows.

Reusability In queries like `UNION`, column count, order, and types must match. It's hard to align all those with a SELECT *

Readability: SELECT * is a black box. Without running it, you can't know the result structure. Explicit columns make your query easier to read and maintain. Look at these ⬇️ By looking at the 2nd query you already know what you will see.

Indexing: Indexes are like a map of your data. They make it faster to find what you need. When you use SELECT *, you lose your map.

Performance: SELECT * fetches everything, including what you don’t need. Smaller queries = faster performance. Ask yourself: “Do I need this column?”

Reliability: Changes to a table's schema (adding/removing columns) can break systems relying on SELECT *. Explicitly naming columns keeps queries and apps stable.

SELECT * is the "Hello World" of SQL. But it's a trap. You should avoid it. Here’s why 👇

Hi All, It's been a while... I turned off content creation a few months ago and had some much-needed time away from it. Several reasons lead to the decision: - I started to work on an interesting project beside my 9-5. That took priority. I may talk about this later as it is

How can you build a good understanding of math for machine learning? Here is a complete roadmap for you. In essence, three fields make this up: • Calculus • Linear algebra • Probability theory Let's take a quick look at them!

There's a stunning, simple explanation behind matrix multiplication. This is the first time this clicked on my brain, and it will be the best thing you read all week. Here is a breakdown of the most crucial idea behind modern machine learning: 1/15

Let's discuss the simplest retrieval method: "Similarity search". It involves retrieving the top "k" documents that are most similar to a given query based on vector embeddings. Let's learn more about it 🧵 👇

This is the best book for learning the mathematics of Machine Learning. There are two things that make this book different from everything out there: 1. The book is 100% focused on the math required in Machine Learning. This is not about general concepts and theories. This is

I've amassed a small collection of Python programming tips which I've been posting here on X. Mostly Matplotlib stuff, but not limited to it. I've created a dedicated page on Github to store them all in one convenient place now. I hope this helps a bit.

https://t.co/zu2USJEAOe

The best way to learn coding for data: