Artificial Intelligence and Machine Learning

• Introduction to AI and ML (4 hours)
  • Definitions, scope, and applications (e.g., healthcare, finance, automation).
  • Types of AI: Narrow, General, and Superintelligence.
  • Types of ML: Supervised, Unsupervised, and Reinforcement Learning.
• Python for AI/ML (6 hours)
  • Python basics: Variables, loops, functions, and libraries (NumPy, Pandas).
  • Setting up development environments (Jupyter Notebook, VS Code).
• Data Fundamentals (6 hours)
  • Data types: Structured vs. unstructured.
  • Introduction to data preprocessing (cleaning, normalization).

Lab: Setting up Python and exploring datasets with Pandas (4 hours).

• Supervised Learning Concepts (6 hours)
  • Regression vs. classification.
  • Linear regression, logistic regression, and evaluation metrics (MSE, accuracy).
• Programming Supervised Learning (8 hours)
  • Implementing linear regression using scikit-learn.
  • Data visualization with Matplotlib and Seaborn.
• Lab: Building a simple linear regression model (6 hours).

• Unsupervised Learning Basics (6 hours)
  • Clustering: K-Means, hierarchical clustering.
  • Dimensionality reduction: PCA (Principal Component Analysis).
• Advanced Data Preprocessing (6 hours)
  • Handling missing data, encoding categorical variables.
  • Feature scaling and selection.
• Lab: Implementing K-Means clustering and PCA on a dataset (8 hours).

• Neural Network Basics (6 hours)
  • Introduction to artificial neural networks (ANNs).
  • Activation functions and perceptrons.
• Building Simple Models (6 hours)
  • Introduction to TensorFlow/Keras for neural networks.
  • Building a basic ANN for classification.
• Lab: Creating a simple neural network for a classification task (6 hours).
• Capstone Project: Develop a basic ML model for a real-world dataset (e.g., house price prediction) (2 hours).