Course Outline
Course Outline Training Proposal
Day 1 - Introduction to AI and Python for Data Workflows
• Overview of artificial intelligence and machine learning landscape
• Role of AI in modern data engineering
• Python fundamentals refresher for AI applications
• Working with data using pandas and NumPy
• Introduction to APIs and JSON data handling
• Mini exercise loading and transforming datasets
Day 2 - Machine Learning Foundations for Practitioners
• Supervised and unsupervised learning concepts
• Feature engineering and data preparation techniques
• Model training basics using scikit-learn
• Model evaluation and performance metrics
• Introduction to model deployment concepts
• Hands-on building a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and how they work
• Tokenization, context windows, and limitations
• Prompt design principles and techniques
• Zero-shot and few-shot prompting
• Prompt evaluation and iteration strategies
• Hands-on prompt engineering exercises
Day 4- Building AI Applications with LLMs
• Using LLM APIs in Python
• Structured outputs and function calling concepts
• Building chat-based and task-based applications
• Introduction to retrieval augmented generation
• Connecting LLMs with external data sources
• Mini project building a simple AI assistant
Day 5 - Productionizing AI Solutions
• Designing scalable AI workflows
• Integrating AI into data pipelines
• Monitoring and improving model performance
• Cost optimization and API usage strategies
• Security and responsible AI considerations
• Final 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
