📌 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.

 

Watch the Video

Download the Practice File

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

Click “Mark as Complete” button.

 

Thanks and Good Luck

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