📌 Topics Covered:

• Vectors & Matrices: Understanding 1D (vectors) and 2D (matrices) data structures as the building blocks of datasets.
• Domain Use Cases:
  • Customer Segmentation: Using vectors to represent customer attributes for clustering.
  • Sales Forecasting & Regression: Utilizing matrix math to calculate coefficients in predictive models.
  • Marketing & Supply Chain: Optimizing inventory and logistics via linear programming and matrix operations.
• Scalar Multiplication: Scaling vectors and matrices by a single numerical value.
• Vector & Matrix Operations: Executing addition, subtraction, and dot products/multiplication to transform data.

📝 Class Summary:

This module bridges the gap between pure mathematics and business intelligence, teaching how the “language of data” (Linear Algebra) enables the complex calculations behind modern optimization and forecasting.

✅ What You Will Learn:

• How to represent a single customer as a Vector of features (e.g., age, income, spending score).
• Techniques for Matrix Multiplication which are essential for rotating and scaling data in feature engineering.
• The methodology behind Regression models, where matrix math solves for the “line of best fit”.
• How Scalar Multiplication is used in gradient descent to “step” toward an optimal solution in machine learning.

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PDF Link – https://drive.google.com/file/d/1YthQgtIT8k7QIDyhGCS5Q3CgZPo1lDhO/view?usp=sharing

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Thanks and Good Luck