Course Outline
Course Syllabus Training Proposal
Day 1 - Foundations of AI and Python for Data Workflows
• Survey of the artificial intelligence and machine learning ecosystem
• The role of AI in contemporary data engineering
• Python fundamentals revision for AI-focused applications
• Data manipulation using pandas and NumPy
• Introduction to API interactions and JSON data management
• Practical task: loading and transforming datasets
Day 2 - Machine Learning Essentials for Practitioners
• Core concepts of supervised and unsupervised learning
• Feature engineering and data preparation methodologies
• Basics of model training using scikit-learn
• Model assessment and key performance indicators
• Overview of model deployment principles
• Practical task: developing a basic predictive model
Day 3 - Overview of LLMs and Prompt Engineering
• Understanding the architecture and function of large language models
• Tokenization, context windows, and operational constraints
• Principles and techniques for prompt design
• Zero-shot and few-shot prompting strategies
• Methods for evaluating and iterating on prompts
• Practical task: prompt engineering exercises
Day 4 - Developing AI Applications with LLMs
• Integrating LLM APIs within Python scripts
• Concepts of structured outputs and function calling
• Developing chat-based and task-oriented applications
• Introduction to retrieval augmented generation (RAG)
• Linking LLMs to external data sources
• Mini project: creating a basic AI assistant
Day 5 - Operationalising AI Solutions
• Architecting scalable AI workflows
• Embedding AI into data pipelines
• Monitoring and optimising model performance
• Cost efficiency and API management strategies
• Security protocols and responsible AI practices
• Final project: engineering a complete 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
