Module 1: Introduction to AI
Overview of AI and its applications
History of AI and its evolution
Types of AI (narrow, general, superintelligence)
Module 2: Machine Learning
- Introduction to machine learning
- Supervised, unsupervised, and reinforcement learning
- Machine learning algorithms (linear regression, decision trees, neural networks)
Module 3: Deep Learning
- Introduction to deep learning
- Convolutional neural networks (CNNs)
- Recurrent neural networks (RNNs)
- Long short-term memory (LSTM) networks
Module 4: Natural Language Processing (NLP)
- Introduction to NLP
- Text preprocessing and tokenization
- Sentiment analysis and topic modeling
- Language models and chatbots
Module 5: Computer Vision
- Introduction to computer vision
- Image processing and feature extraction
- Object detection and recognition
- Image classification and segmentation
Module 6: AI Applications
- AI in healthcare (medical diagnosis, personalized medicine)
- AI in finance (predictive modeling, risk analysis)
- AI in transportation (self-driving cars, route optimization)
- AI in education (adaptive learning, intelligent tutoring)
Module 7: AI Ethics and Bias
Understanding AI ethics and bias
Fairness and transparency in AI
Mitigating bias in AI systems
Module 8: AI Future and Trends
Future trends in AI (explainable AI, edge AI)
Potential risks and challenges of AI
Opportunities and benefits of AI
Course Format:
- Online lectures
- Practical exercises
- Projects and assignments
Duration:
- 8-12 weeks
- 24-36 hours
This comprehensive course covers the fundamentals of AI, including machine learning, deep learning, NLP, computer vision, and AI applications. Students will gain practical skills and knowledge to develop and apply AI solutions in various domains.