WEEK

TOPIC

DATE AND TIME

DESCRIPTION

FACILITATORS

1

Fundamentals of Machine Learning and Data Preprocessing

1 HR

●     Introduction to Machine Learning

Key concepts and types (supervised, unsupervised, reinforcement learning)

Real-world applications

●     Data Preprocessing and Feature Engineering

Data cleaning techniques

Feature selection and engineering

Modeling and prediction

2

Data Governance

1 HR

●     Data collection and privacy

Data Collection

Best Practices for Data Collection

Designing effective data collection strategies

Ensuring data quality and reliability

●     Privacy and Security in AI

Data privacy concerns and regulatory frameworks

Techniques for ensuring data security

3

Responsible AI and Ethical Considerations

1 HR

●     Principles of Responsible AI

Ethics in AI development and deployment

Fairness, accountability, and transparency

●     Bias and Fairness in AI

Understanding and detecting bias

Techniques to mitigate bias

4

Advanced Topics in AI and Data Integration.

1 HR

●     Explainability and Interpretability in AI

Importance and techniques for explainable AI models

Tools for model interpretability

●     Integrating Data from Multiple Sources

Data integration techniques

Handling data heterogeneity and inconsistency

Real-world use cases