This page contains the lecture-wise learning materials for the course. Each lecture includes a summary of the topics covered, presentation slides, recommended readings, and practice questions to reinforce learning. 

1.  Module 1: Business Analytics & the ML Project Lifecycle 

• Lecture 1 (07/08/2026) :

  • • Lecture Summaries : Course Introduction & Overview, Introduction to Business Analytics, Role of Analytics in Business Decision-Making

    • Lecture Slides : Introduction Slides [Link]

    • Recommended Readings :

    • Practice Questions :

• Lecture 2 (08/08/2026) :

  • • Lecture Summaries : Business Analytics & Business Intelligence, Business Analytics vs. Business Intelligence, Business Analytics Spectrum, Descriptive, Diagnostic, Predictive & Prescriptive Analytics, Entry of Machine Learning into Business Analytics 

    • Lecture Slides : Business Analytics (Slides 1–10) [Link]

    • Recommended Readings :

    • Practice Questions : [Link]

• Lecture 3 (14/08/2026) :

  • • Lecture Summaries : Machine Learning Project Lifecycle, Training, Validation & Holdout Data, Need for Holdout Data, Holdout Contamination, Limitations of a Single Train–Validation–Test Split, Towards Cross-Validation 

    • Lecture Slides : Business Analytics (Slides 11–32) [Link]

    • Recommended Readings :

    • Practice Questions : [Link]

• Lecture 4 (21/08/2026) :

  • • Lecture Summaries : K-Fold Cross-Validation, Cross-Validation vs. Holdout Method, Advantages & Limitations of Cross-Validation

, Towards Automated Machine Learning (AutoML)

• • • Lecture Slides : Business Analytics (Slides 33–39) [Link]

    • Recommended Readings :

    • Hands-on Resources  : Dataset [Link];  Jupyter Notebook [Link]

2. Module 2: Business Data Preparation & Performance Evaluation  

3. Module 3: Prediction & Forecasting for Business Decisions  

4. Module 4: Customer Analytics, Market Intelligence & Experimentation 

5. Module 5: Responsible AI & Business Deployment