Prerequisites

• Basic programming knowledge (Python recommended)
• Fundamental mathematics skills
• Understanding of linear algebra
• Basic statistics knowledge
• Computer with minimum 8GB RAM
• Recommended: Jupyter Notebook installation

12-Week Artificial Intelligence Course Syllabus

Week 1: AI Foundations

• Introduction to artificial intelligence
• AI history and evolution
• Types of AI systems
• Machine learning fundamentals
• Symbolic AI approaches
• Knowledge representation
• Ethical considerations in AI

Week 2: Machine Learning Basics

• Supervised learning techniques
• Unsupervised learning methods
• Classification algorithms
• Regression models
• Decision tree implementations
• Model evaluation metrics
• Performance optimization strategies

Week 3: Neural Network Fundamentals

• Neural network architecture
• Activation functions
• Backpropagation techniques
• Deep learning principles
• TensorFlow introduction
• PyTorch fundamentals
• Practical neural network design

Week 4: Computer Vision

• Image processing techniques
• Convolutional neural networks
• Object detection algorithms
• Face recognition systems
• Image classification methods
• OpenCV implementation
• Practical computer vision projects

Week 5: Natural Language Processing

• Text preprocessing techniques
• Sentiment analysis
• Language modeling
• Named entity recognition
• Machine translation basics
• NLTK and spaCy libraries
• Transformer architecture introduction

Week 6: Advanced Machine Learning

• Ensemble learning methods
• Random forest techniques
• Support vector machines
• Clustering algorithms
• Dimensionality reduction
• Feature engineering
• Advanced model optimization

Week 7: Reinforcement Learning

• Q-learning fundamentals
• Policy gradient methods
• Markov decision processes
• Game theory applications
• OpenAI Gym environment
• Practical reinforcement learning
• AI gaming strategies

Week 8: Generative AI

• Generative adversarial networks
• Variational autoencoders
• Text generation models
• Image synthesis techniques
• Stable diffusion principles
• Ethical AI generation
• Practical generative projects

Week 9: AI Ethics and Responsible Development

• Algorithmic bias detection
• Fairness in machine learning
• Privacy preservation techniques
• Regulatory compliance
• Ethical decision-making frameworks
• Responsible AI development
• Case studies in AI ethics

Week 10: Advanced Neural Architectures

• Transformer models
• BERT and GPT architectures
• Large language models
• Multi-modal learning
• Advanced deep learning techniques
• State-of-the-art model implementations
• Research paper analysis

Week 11: AI Cloud and Deployment

• Cloud AI platforms
• Model deployment strategies
• Scalable AI infrastructure
• Microsoft Azure AI
• AWS AI services
• Google Cloud AI
• Practical deployment techniques

Week 12: Capstone Project

• End-to-end AI project
• Real-world problem solving
• Portfolio development
• Industry best practices
• Professional networking
• Career guidance
• Final project presentations

Learning Outcomes

• Comprehensive AI technology understanding
• Machine Learning expertise
• Neural network design capabilities
• Ethical AI development skills
• Practical implementation knowledge
• Industry-ready artificial intelligence skills

Certification

Students receive a comprehensive Artificial Intelligence certification upon successful course completion.

Note: Course content may be dynamically updated to reflect emerging technological trends.