Hi everyone,

Today is officially Day 1 of my AI Prep Challenge — my commitment to learn, build, and share every single day until I secure an AI/ML internship in my 3rd year.

I decided to start this challenge the right way — not by rushing into flashy frameworks or AI models, but by revisiting the foundations that actually make AI work.

Part 1 – Python Basics Revision

Before I touch NumPy, Pandas, or TensorFlow, I want my Python skills to feel like second nature.
So today, I revised:

Variables & Data Types — strings, ints, floats, booleans
Operators — arithmetic, comparison, logical
Functions — reusable code blocks for cleaner logic
Loops & Conditionals — the control flow that glues everything together

I even wrote small snippets to keep it hands-on — nothing fancy, just solidifying the basics.

Part 2 – Math for AI/ML: Linear Algebra Basics

Math is the language of AI, and in Machine Learning, Linear Algebra is everywhere.
Today I refreshed my understanding of:

Vectors — representing data points or weights
Matrices — how they store and transform data
Dot Product — the core operation in neural networks
Matrix Multiplication — the math behind feeding data into models

The best part? I implemented these using NumPy, which made the operations lightning fast compared to plain Python. It’s satisfying to see the theory turn into working code right away.

How It Felt

It reminded me why skipping basics is dangerous — you think you’re saving time, but in reality, you’re building a shaky tower. Today felt like laying down strong bricks for everything that’s coming next.

Tomorrow’s Plan

Next, i will cover matrix operations and transformations, also pratics python on leetcode try and solve some problems

💬 If you’re following along — don’t underestimate the power of Day 1 basics. These are the skills that make the complex stuff possible.

See you in Day 2 🚀

💻 Want to see my full work for Day 1?
I’ve uploaded my complete Python notebook (covering Python basics revision + linear algebra in NumPy) here:
📂 View the Day 1 Notebook on GitHub

📺 Resources I Used

YouTube:

Linear combinations, span, and basis vectors | Chapter 2, Essence of linear algebra

Day 1 – Back to the Basics: Python & Linear Algebra for AI/ML