•Understanding the fundamentals of AI
•Historical development and key milestones
•Current applications and future trends

• Essential Python programming concepts
• Data structures and libraries for AI (e.g., NumPy, Pandas)
• Data manipulation and preprocessing

• Introduction to supervised and unsupervised learning
• Model selection and evaluation metrics
• Feature engineering and selection

• Neural networks and architecture
• Convolutional Neural Networks (CNNs) for computer vision
• Recurrent Neural Networks (RNNs) for natural language processing

• Text preprocessing and tokenization
• Sentiment analysis, text classification, and named entity recognition
• Building chatbots and language generation models

• Image processing and feature extraction
• Object detection and image classification
• Building image-based applications

• To reinforcement learning concepts
• Q-learning, Deep Q Networks (DQNs), and policy gradients
• Applications in robotics and gaming

• Ethical considerations in AI
• Bias and fairness in AI algorithms
• Responsible AI practices and guidelines

• AI in healthcare, finance, and autonomous vehicles
• Case studies and industry-specific applications
• Building AI projects for real-world scenarios

• Collaborative project development
• End-to-end project lifecycle, from problem formulation to deployment
• Building and presenting a capstone project

• Data privacy regulations and compliance
• Securing AI models and data
• Ethical considerations in AI project design

• Resume building and interview preparation
• Effective communication in a technical role
• Time management and project planning
• Building a professional online presence

• Image Recognition for Disease Diagnosis
• AI-Powered Chatbot
• Emotion Recognition in Videos
• Autonomous Vehicle Control
• Recommendation System for Education