Why AI/ML? + Setting Up My Tools (Python, Jupyter, VSCode, Colab)

A few years ago, I asked ChatGPT a coding question and got a perfect answer quickly, which amazed me. This sparked my curiosity about how it works, leading me to learn AI and machine learning from the ground up. My goal is not only to understand the theory but also to create something real and share my journey, whether I succeed or fail.

Why I’m Learning AI/ML

AI is now a key part of our daily lives, influencing how we interact with the world. It powers music recommendations on platforms like Spotify, runs chatbots for customer service, and enables smart assistants like Alexa and Google Assistant. In the automotive industry, AI is crucial for developing self-driving cars. As AI advances, it will become even more integrated into our activities, making it an exciting field to explore.

For me, this is about more than just the hype. I want to:

• Understand how these systems actually work

• Build real projects with them

• Eventually help others who feel as lost as I did at the beginning

I'm not an expert, not even close. But I'm committed to learning and documenting everything along the way.

Tools I Set Up Today

Real learning starts with a clean setup — and today was all about getting ready. There are numerous tools available, but I focused on trying out the following ones:

1. Python

The core language of AI and machine learning. Almost every tool, tutorial, and framework in this space uses Python.

• Installed Python 3.x from python.org

2. VS Code

My preferred code editor — fast, flexible, and perfect for managing both Python scripts and notebooks.

• Installed from code.visualstudio.com

• Added the Python extension for smoother development

3. Jupyter Notebook

Great for writing and running code in sections. Especially useful for exploring data, testing models, and seeing output instantly.

• Installed and launched locally to test it out

4. Google Colab

Colab is a free, cloud-based Jupyter notebook environment provided by Google. It's ideal for running small to medium-sized machine learning experiments without any local setup.

Why I’ll be using it:

• It’s free to use

• Runs entirely in the browser — no local installation needed

• Comes with access to a free GPU (limited usage, but enough for practice)

• Easy integration with Google Drive

Ran my first “Hello, AI world” cell on Colab

5. Kaggle Notebooks

Kaggle, owned by Google, also offers a powerful browser-based notebook environment — especially useful for working with real-world datasets and entering competitions.

Why I’m exploring it:

• It’s free for CPU and GPU use

• Offers K80 GPUs and limited access to TPUs

• Fully browser-based — no setup required

• Seamless access to thousands of public datasets

• Ideal for trying out Kaggle competitions and shared notebooks

Set up my Kaggle account
Explored the notebook interface and ran a few cells

Reflection

No models trained yet. No data analysed.
But honestly? I feel good.

Getting everything set up and ready feels like a solid Day 1 win — the kind that makes you want to keep going.

Tomorrow, I’ll dive into Python basics and share my notes, lessons, and any stumbles right here.

If you’re just getting started with AI/ML too, I’d love for you to follow along. Let’s figure this out together.

On to Day 2