🚀 Stage 1 – Becoming Friends with Computers (Computer & Logic Basics)
Goal: Understand how computers work and think of them as your friend.
Learn:
How a computer works (Input → Process → Output)
Hardware (CPU, RAM, GPU) basics
What software is (Apps, OS, Browsers)
How the internet works (Client-Server Model)
Why important?
Before building AI, you must understand the machine you’ll run it on.
Resources:
“Computer Basics for Kids” on YouTube
Typing practice – keybr.com
🚀 Stage 2 1– Coding ABCs (Programming Basics)
Goal: Learn how to give commands to a computer.
Language: Python (best & easiest for AI)
Learn:
Variables (store data)
Loops (repeat tasks)
Conditions (if-else logic)
Functions (mini code packages)
Data structures – list, dictionary, tuple
File handling (read/write CSV, TXT)
Why important?
Before teaching AI, you must know how to “talk” to the computer.
Resources:
Python.org tutorials
Book: Python for Kids by Jason Briggs
Practice: Replit.com, Codecademy Python
🚀 Stage 3 – Maths Superpowers (Math for AI)
Goal: Become a math superhero for AI.
Topics:
Arithmetic & Algebra – basics and equations
Probability – the science of guessing
Statistics – averages, median, mode, trends
Linear Algebra – matrices & vectors (AI sees images, audio, text as numbers)
Calculus – derivatives, gradient descent (help AI learn correctly)
Why important?
AI thinks in numbers. Math is its DNA.
Resources:
Khan Academy – Math & Probability
3Blue1Brown (YouTube) – visual math explanations
🚀 Stage 4 – Data Magic (Data Handling & Cleaning)
Goal: Become the master of data.
Learn:
Reading/writing CSV & JSON files
Pandas library – modify, filter, sort data
NumPy – numerical calculations
Databases – MySQL, MongoDB
Data cleaning – filling missing values, removing duplicates
Why important?
AI is useless without data. Clean data makes AI smarter.
Resources:
Kaggle datasets for practice
Pandas & NumPy official docs
🚀 Stage 5 – Machine Learning Magic
Goal: Teach AI through examples.
Learn:
Supervised Learning – labeled data (dog/cat images)
Unsupervised Learning – AI groups data on its own (clustering)
Reinforcement Learning – trial & error learning (games, robotics)
Libraries:
Scikit-learn
Matplotlib / Seaborn for visualization
Projects:
Predict house prices
Spam email classifier
Movie recommendation system
Resources:
Andrew Ng’s Machine Learning (Coursera)
Book: Hands-On Machine Learning
🚀 Stage 6 – Deep Learning (Building AI’s Brain)
Goal: Build the brain of AI.
Learn:
Neural Networks basics (perceptrons, layers, activation functions)
CNNs – for images
RNN/LSTM – for text & speech
Transformers – for advanced AI like ChatGPT
Libraries:
TensorFlow
PyTorch
Projects:
Handwritten digit recognition (MNIST)
Image classifier (cats vs dogs)
Simple chatbot
🚀 Stage 7 – AI Tools & APIs (The Weapon Box)
Goal: Build AI with ready-made industry tools.
Learn:
OpenAI API (ChatGPT, DALL·E)
HuggingFace models
LangChain (build AI apps)
Google Colab (cloud-based coding)
🚀 Stage 8 – Deployment & Real-World Projects
Goal: Show your AI to the world.
Learn:
Flask/Django – Python web frameworks
Streamlit – easy AI app building
Git/GitHub – save & share code
Cloud deployment – AWS, Azure, Google Cloud
Projects:
AI image generator website
Voice-controlled assistant
Stock price predictor
🚀 Stage 9 – Continuous Learning & Specialization
Specialize in:
Computer Vision (image/video AI)
NLP – Natural Language Processing (text/chat AI)
AI for Healthcare
AI in Finance
Robotics + AI