Module 1: Leveraging AI
- Overview of AI and its current capabilities
- Introduction to popular AI assistants (Claude, ChatGPT, etc.)
- Basic principles of prompt engineering
- Document analysis and summarization
- Research assistance and information gathering
- Writing and editing support
- Data analysis and visualization
- Task planning and management
- Banking and Finance:
- Oil and Gas Industry:
- Healthcare:
- Stock Market:
- Image Generation (e.g., DALL-E, Midjourney, Stable Diffusion)
- Voice and Speech Recognition (e.g., Whisper)
- Natural Language Processing (e.g., spaCy, NLTK)
- Computer Vision (e.g., OpenCV, TensorFlow Object Detection API)
- AI bias and fairness
- Data privacy and security
- Responsible AI use in professional settings
- Practical exercises using various AI tools
- Industry-specific case studies and problem-solving
- Emerging AI technologies
- Resources for staying updated on AI advancements
Module 2: Understanding AI development
- What is Machine Learning?
- The Machine Learning Process
- Python for Machine Learning
- Introduction to Supervised Learning
- Linear Regression
- Practical Session
- Classification Concepts
- Logistic Regression
- Model Evaluation for Classification
- Practical Session
- Decision Trees
- Ensemble Methods
- Practical Session
- Introduction to Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Practical Session
- From Biological to Artificial Neurons
- Feedforward Neural Networks
- Training Neural Networks
- Practical Session
- Deep Learning Basics
- Applications of Deep Learning
- Course Review and Next Steps
- Final Project