Week 1: Overview of AI

  • Day 1: Introduction to AI – History and Evolution
  • Day 2: Key Concepts: Machine Learning, Deep Learning, NLP
  • Day 3: AI Applications Across Industries (Healthcare, Finance, etc.)
  • Day 4: Ethical Implications of AI
  • Day 5: Workshop – AI in Everyday Life (Discussion)

Week 2: Machine Learning (ML)

  • Day 1: Introduction to ML: Supervised vs. Unsupervised Learning
  • Day 2: Data Preprocessing and Feature Engineering
  • Day 3: Regression Algorithms (Linear and Logistic)
  • Day 4: Classification Algorithms (Decision Trees, SVMs)
  • Day 5: Hands-on Lab – Building Simple ML Models (Python/Scikit-learn)

Week 3: Deep Learning (DL) and Neural Networks

  • Day 1: Basics of Neural Networks
  • Day 2: Deep Learning Architectures (CNNs, RNNs)
  • Day 3: Training Neural Networks and Hyperparameter Tuning
  • Day 4: Introduction to TensorFlow and Keras
  • Day 5: Lab – Build and Train a Simple Neural Network (Digit Recognition Task)

Week 4: Advanced Topics and Capstone Project

  • Day 1: Natural Language Processing (NLP)
  • Day 2: AI in Robotics and Autonomous Systems
  • Day 3: AI Ethics and Future Trends
  • Day 4: Capstone Project – Build a Small AI Application (AI Chatbot, Image Classifier, etc.)
  • Day 5: Capstone Presentations and Feedback

Assessment: Weekly quizzes, hands-on labs, and final capstone project evaluation.

Materials: Python, Scikit-learn, TensorFlow/Keras, Jupyter Notebook.