Course Outline
Course Outline Training Proposal
Day 1 - Foundations of AI and Python for Data Processes
• Overview of the artificial intelligence and machine learning ecosystem
• The role of AI in contemporary data engineering
• Python fundamentals review for AI use cases
• Manipulating data with pandas and NumPy
• Introduction to APIs and JSON data management
• Practical exercise: Loading and transforming datasets
Day 2 - Machine Learning Fundamentals for Professionals
• Concepts of supervised and unsupervised learning
• Techniques for feature engineering and data preparation
• Basic model training using scikit-learn
• Assessing model evaluation and performance metrics
• Introduction to model deployment principles
• Practical exercise: Developing a basic predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their operational mechanics
• Tokenization, context windows, and inherent limitations
• Principles and techniques of prompt design
• Zero-shot and few-shot prompting methods
• Strategies for prompt evaluation and iterative refinement
• Practical exercise: Prompt engineering tasks
Day 4- Constructing AI Applications with LLMs
• Utilizing LLM APIs within Python
• Concepts of structured outputs and function calling
• Developing chat-based and task-oriented applications
• Introduction to retrieval-augmented generation
• Linking LLMs with external data repositories
• Mini project: Building a basic AI assistant
Day 5 - Deploying AI Solutions to Production
• Designing scalable AI workflows
• Integrating AI into data pipelines
• Monitoring and enhancing model performance
• Cost optimization and API usage strategies
• Security and responsible AI considerations
• Capstone project: Building an end-to-end AI solution
Testimonials (2)
The trainer was very available to answer all te kind of question I did
Caterina - Stamtech
Course - Developing APIs with Python and FastAPI
Trainer develops training based on participant's pace
