• Understanding statistical thinking in data science
• Applying probability models to real-world datasets
• Drawing insights from descriptive and inferential analyses 

Learning Outcomes:

Apply statistical models to data; Infer relationships and trends; Make 
quantitative decisions using probability

• Preparing datasets for analytics
• Building meaningful visualizations
• Using charts for storytelling

Learning Outcomes:

Clean and prepare raw datasets; Develop effective data visualizations; 
Communicate insights clearly

• Understanding end-to-end ML workflows
• Applying supervised and unsupervised learning
• Engineering features for model optimization

Learning Outcomes:

Design and train ML models; Apply predictive analytics; Assess model 
performance accurately

• Understanding multi-layer neural networks
• Implementing models using deep learning frameworks
• Grasping optimization and training concepts

Learning Outcomes:

Build and train deep neural networks; Tune parameters for optimal 
performance; Apply DL frameworks

• Understanding architectures of DL applications
• Implementing models in vision and text domains
• Applying transfer learning for efficiency

Learning Outcomes:

Apply DL to CV and NLP tasks; Deploy AI models across domains; Compare 
architectures and performance

• Understanding MLOps lifecycle
• Automating deployment and versioning
• Managing production ML/AI systems

Learning Outcomes:

Develop automated ML pipelines; Manage model life cycles; Understand 
LLMOps for generative models

• Exploring domain-specific AI use cases
• Understanding emerging technologies shaping industries
• Leveraging AWS and Causal AI in applied projects

Learning Outcomes:

Analyze industry AI trends; Evaluate generative and causal AI applications; 
Design domain-specific AI solutions

• Tracing the conceptual evolution of agentic systems
• Differentiating static and autonomous AI agents
• Establishing foundational understanding of Agentic AI models

Learning Outcomes:

Explain the foundations of Agentic AI, Historical Development of Agentic AI; 
Identify use cases of adaptive agents

• Deconstructing multi-agent systems
• Understanding underlying AI agent architectures
• Exploring core technologies behind autonomous reasoning

Learning Outcomes:

Build mental models of agent architecture; Implement simple multi-agent 
flows; Analyze emerging agent technologies

• Understanding governance principles for agentic AI
• Monitoring agent performance and ethical behavior
• Preparing for emerging regulatory and operational trends

Learning Outcomes:

Establish governance frameworks for AI agents; Measure and optimize 
performance; Apply ethical principles in agent design