Section outline

• Select section Day 1: Introduction to Data Science and Machine Learning Basics

  Day 1: Introduction to Data Science and Machine Learning Basics

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  Session 1: Introduction to Data Science

  • Data Science & its applications

  • Pillars of Data Science & Big Data

  • Tools, techniques, and solutions overview

  Session 2: Machine Learning Basics

  • Types of ML solutions

  • Tools for ML: Python, R

  • Business case studies and identifying ML types quiz

  Session 3: Supervised Machine Learning (SML)

  • Overview of supervised learning methods

  • Demonstration of SML methods with real-world datasets
• Select section Day 2: Advanced Methodologies and AI Integration

  Day 2: Advanced Methodologies and AI Integration

  Session 4: Unsupervised Machine Learning (USML)

  • Overview of unsupervised learning methods

  • Demonstration of USML methods with datasets

  • Exercise: Mapping ML methods to business problems

  Session 5: AI Methodologies - Non-Generative

  • Overview of non-generative AI methods

  • Use cases deep dive

  Session 6: AI Methodologies - Generative

  • Overview of generative AI methods

  • Use cases deep dive

  Program Wrap-Up

  • Integration of AI and ML systems with practical use cases

  • Final program quiz and wrap-up discussion