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 contemporary data engineering
• Python fundamentals refresher for AI applications
• Manipulating 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
• Basics of model training using scikit-learn
• Model evaluation and performance metrics
• Introduction to model deployment concepts
• Hands-on session: building a simple predictive model
Day 3 - Introduction to LLMs and Prompt Engineering
• Understanding large language models and their operational mechanics
• Tokenization, context windows, and inherent limitations
• Prompt design principles and techniques
• Zero-shot and few-shot prompting
• Strategies for prompt evaluation and iteration
• Hands-on prompt engineering exercises
Day 4- Building AI Applications with LLMs
• Utilizing LLM APIs in Python
• Concepts of structured outputs and function calling
• Developing 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 enhancing 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
