AI and Data Projects: Introductory Course in Machine Learning and Data Mining

• Introduction to Learning from Data
• Data types: structured, unstructured, and time series
• The CRISP-DM Process for Data Projects
• Data Mining in a Business Context: Similarity Analyses and Use Cases
• Data Preparation, Models & Tools (Clustering)
• Evaluation, Deployment, and Effort & Risks

• A Comparison of Regression and Classification
• Goals & Areas of Application
• Data Preparation
• Initial Models: Linear Regression and Decision Trees
• Evaluation Using Appropriate Metrics
• Deployment, Effort, and Risks

After a group review of the material covered in the initial self-study phases, participants will delve deeper into the subject matter through hands-on exercises and practical examples.

• How LLMs Work and Text Generation
• Decoding Methods and Sampling
• Training and Common Challenges
• Potential Applications of Generative AI in Business
• Prompt Engineering: Methods, Limitations, and Best Practices
• Evaluation, Deployment & Environment, and Effort & Risks

• Combining Data Sources with LLMs (RAG)
• Retriever and Generator Architecture
• Data Preparation and Model Selection for RAG Systems
• Evaluation and Deployment of RAG Applications
• Fundamentals of Intelligent Agents, Function Calling, and Tool Use
• Agent Architectures, Multi-Agent Systems, and Opportunities & Risks

Once all the questions in the self-study modules have been answered, students will reinforce their knowledge through practical exercises and apply it to real-world scenarios.