Course Outline
Course Outline Training Proposal
Day 1 - Introduction to AI and Python for Data Workflows
• Overview of the artificial intelligence and machine learning landscape
• The 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 their mechanics
• Tokenisation, 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 - Productionising AI Solutions
• Designing scalable AI workflows
• Integrating AI into data pipelines
• Monitoring and improving model performance
• Cost optimisation 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
