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Course Outline

Instructional Syllabus Training Proposal for Government

Day 1 - Foundations of AI and Python in Data Processing

• Current status of the artificial intelligence and machine learning ecosystem

• Application of AI within contemporary data engineering practices

• Review of essential Python programming techniques for AI deployment

• Utilizing pandas and NumPy libraries for data manipulation

• Fundamentals of API interaction and JSON data processing

• Practical activity: Ingesting and processing structured datasets

Day 2 - Core Machine Learning Principles for Operational Use

• Theoretical foundations of supervised and unsupervised learning models

• Strategies for feature engineering and data preprocessing

• Foundational model training processes utilizing scikit-learn

• Assessment frameworks and key performance indicators

• Overview of model deployment methodologies

• Practical exercise: Developing a basic predictive analytics model

Day 3 - Fundamentals of Large Language Models and Prompt Structuring

• Operational mechanisms of large language models and their architecture

• Concepts of tokenization, context limits, and system constraints

• Best practices and methodologies for prompt architecture

• Application of zero-shot and few-shot prompting techniques

• Evaluation protocols and iterative refinement strategies

• Practical exercises: Engineering effective prompts for specific tasks

Day 4- Development of AI Applications Utilizing LLMs

• Integration of LLM APIs within Python environments

• Standards for structured data output and function calling protocols

• Development of interactive chat interfaces and task-oriented systems

• Overview of retrieval-augmented generation techniques

• Linking LLM capabilities with external data repositories

• Capstone activity: Constructing a basic AI-powered assistant

Day 5 - Deployment of AI Solutions in Production Environments

• Architectural design for scalable AI processing workflows

• Incorporation of AI components into existing data pipelines

• Continuous monitoring and performance enhancement protocols

• Budgetary efficiency and API consumption management strategies

• Security frameworks and ethical guidelines for responsible AI

• Capstone project: Implementation of a comprehensive AI solution

 35 Hours

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