ai for beginers

Introduction to AI

• What is Artificial Intelligence?
• History and evolution of AI
• Types of AI: Narrow, General, Super AI
• Applications of AI in real life

Basics of Python Programming (if needed)

• Variables, data types, control structures
• Functions and modules
• Libraries: NumPy, Pandas, Matplotlib

Foundational Math for AI

• Linear Algebra (vectors, matrices)
• Probability & Statistics (distributions, mean/variance, Bayes’ Theorem)
• Calculus basics (derivatives, gradients)
• These should be taught intuitively, with minimal theory at first

Machine Learning Basics

• What is Machine Learning?
• Supervised      vs Unsupervised vs Reinforcement Learning
• The ML pipeline: data → model → evaluation
• Common algorithms:
  • Linear Regression
  • Decision Trees
  • K-Nearest Neighbors
  • Naive Bayes

Neural Networks & Deep Learning (Intro Level)

• Perceptron model
• Activation functions
• Multi-layer perceptrons (MLP)
• Introduction to frameworks: TensorFlow or PyTorch

Working with Data

• Data collection and cleaning
• Feature selection and preprocessing
• Data visualization techniques

Model Evaluation

• Training vs Testing
• Cross-validation
• Evaluation metrics: Accuracy, Precision, Recall, F1-score

Ethical AI

• Bias and fairness in AI
• Privacy concerns
• Real-world consequences of bad AI design

Hands-on Projects (Mini)

• Spam classifier
• Image classifier (cats vs dogs)
• Simple chatbot
• Stock price predictor (basic)

Tools and Ecosystem Overview

• Jupyter Notebooks
• Scikit-learn
• TensorFlow / PyTorch (very light intro)
• Hugging Face (for NLP)