Module 1: Advanced AI Concepts

• AI paradigms and intelligent systems
• Problem-solving in AI
• Knowledge representation
• AI system architecture
• Real-world AI applications

Module 2: Data Science for AI

• Data acquisition techniques
• Data wrangling and preprocessing
• Exploratory data analysis (EDA)
• Data visualization techniques
• Feature engineering basics

Module 3: Machine Learning Algorithms

• Regression models (linear & logistic)
• Classification algorithms:
  • Decision trees
  • K-Nearest Neighbors (KNN)
• Clustering algorithms:
  • K-means clustering
• Model evaluation techniques:
  • Accuracy
  • Precision
  • Recall

Module 4: Deep Learning Fundamentals

• Introduction to neural networks
• Perceptron model
• Activation functions
• Deep learning frameworks:
  • TensorFlow / Keras (intro level)
• Basic image classification model

Module 5: Natural Language Processing (NLP)

• Text processing basics
• Tokenization and stemming
• Sentiment analysis
• Chatbot development basics
• NLP applications

Module 6: Computer Vision Basics

• Image processing fundamentals
• Object detection basics
• Image classification models
• OpenCV introduction
• Real-world applications

Module 7: AI Deployment & Tools

• Model deployment basics
• Cloud platforms overview:
  • AWS / Azure / Google Cloud
• API integration basics
• AI pipelines
• Version control (Git basics)

Module 8: AI Ethics, Governance & Industry Applications

• Responsible AI principles
• Bias and fairness in AI systems
• Data privacy laws
• AI governance frameworks
• Industry applications in:
  • Engineering
  • Healthcare
  • Finance
  • Smart cities

Final Project

• Learners must develop a complete AI solution project, such as:
  • Predictive model (engineering/business data)
  • Chatbot system
  • Image classification tool
• Project must include:
  • Data collection
  • Model development
  • Evaluation
  • Deployment demonstration
• Submission of a technical report + presentation