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Artificial Intelligence – From Basics to Brains
Master AI from Scratch to Deployment Learn Artificial Intelligence step-by-step — starting from core concepts and math, through building neural networks with NumPy and PyTorch, all the way to real-world projects like chatbots, content moderation, and deployment with FastAPI and Streamlit. Perfect for developers, students, and aspiring ML engineers.
Free
Course Content
Module 1: 🧩 Module 1: Introduction to AI
1
What is AI?
2
History and Evolution of Artificial Intelligence
3
Difference between AI, ML, and DL
4
Applications of AI in Real Life
Module 2: 📊 Module 2: Math Behind AI
1
Linear Algebra for AI
2
Calculus for AI
3
Probability & Statistics for AI
4
🔧 Optimization – Gradient Descent & Loss Functions
Module 3: 🧠 Module 3: Machine Learning Basics
1
Supervised vs Unsupervised Learning
2
Regression vs Classification
3
Overfitting vs Underfitting
4
Train/Test Split and Cross-Validation
Module 4: 🧰 Module 4: Building AI From Scratch
1
The Perceptron – Building Block of Neural Networks
2
Building a Neural Network from Scratch using NumPy
3
Backpropagation – How AI Learns
4
Activation Functions – Adding Non-Linearity
5
Mini Project – Spam Detection with a Simple Neural Network
Module 5: 🔥 Module 5: Deep Learning with Frameworks
1
Getting Started with PyTorch
2
Building Neural Networks with nn.Module
3
Image Classification with PyTorch (MNIST Digits)
4
Using GPU Acceleration in PyTorch
5
Loss Functions and Optimizers Explained
6
Capstone Project – Image Classification with CNN on CIFAR-10
Module 6: 🧪 Module 6: AI Projects
1
Project: Chatbot using Transformers (Conversational AI)
2
Project: Sentiment Analysis with DistilBERT (Text Classification)
Module 7: 🚀 Module 7: Deploying AI Models
1
AI Models Deployments 3rd Party
2
Deploy Your AI Model with Streamlit
3
Bonus: Deploy with Docker for Production