Artificial Intelligence & Machine Learning

Description:
Explore the world of AI and ML by understanding algorithms, building models, and making predictions with Python libraries like Scikit-learn and TensorFlow.

Learning Objectives:

  • Understand AI types and real-world applications

  • Learn supervised and unsupervised learning

  • Build models using Python

  • Evaluate and improve accuracy

Detailed Content:

15.1 What is Artificial Intelligence?

AI simulates human intelligence in machines. Categories include:

  • Narrow AI: Performs specific tasks (Siri)

  • General AI: Human-like intelligence (still theoretical)

15.2 Machine Learning Overview

ML is a subset of AI where machines learn patterns from data.

  • Supervised Learning: Learn from labeled data (regression, classification)

  • Unsupervised Learning: Discover patterns (clustering)

15.3 ML Workflow

  1. Collect data

  2. Preprocess (clean, normalize)

  3. Split into train/test sets

  4. Choose algorithm

  5. Train model

  6. Evaluate (accuracy, precision, recall)

  7. Tune hyperparameters

15.4 Algorithms

  • Regression: Predict numeric values

  • Classification: Predict categories (e.g., spam vs. not spam)

  • Clustering: Group similar data (e.g., customer segmentation)

15.5 Libraries and Tools

  • Scikit-learn: Popular for beginners

  • Pandas, NumPy: Data processing

  • TensorFlow, PyTorch: Deep learning

  • Jupyter Notebooks: Interactive development