17/07/2026
The evolution of mathematics into Machine Learning:😅
Math: “Let me teach you numbers.”
Physics: “Now let’s use those numbers to understand how the world works.”
Engineering: “Great. Let’s use that knowledge to build things.”
AI & ML: “Interesting… now give me the data. I’ll find the patterns myself.” 😅
But the funny part is that Machine Learning didn't replace mathematics.
It built on top of it.
Behind the models we use today are concepts from:
📐 Mathematics — algebra, calculus, probability, statistics
⚛️ Physics — modelling systems and understanding patterns
⚙️ Engineering — building systems that work in the real world
🤖 AI & ML — using data and algorithms to make predictions and decisions
Even when I was training AI models for my trading bot, the process wasn't simply:
> “Give the AI data and let it trade.”
There was mathematics behind the features, statistics behind the predictions, algorithms behind the model, and engineering behind deploying the entire system.
And then there's Claude…
Claude: “I just got here. What are we calculating?” 😂
The lesson?
If you're learning AI and Machine Learning, don't be afraid of mathematics.
You don't need to become a mathematician overnight.
But understanding the fundamentals will help you understand why your model works—not just how to import a library and call .fit().
The deeper you go:
Math → Physics → Engineering → AI/ML
The more you realize that technology is not built from magic.
It's built from layers of knowledge.
Dwise – The Architect of Intelligent Systems