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Linear Algebra for Data Science Course

Linear Algebra for Data Science Course
4 to 360 hours of flexible workload
valid certificate in your country

What Will I Learn?

This Linear Algebra for Data Science Course gives you practical tools to build, understand, and improve predictive models. Learn how to construct feature matrices, apply linear models, interpret weights, detect redundancy, and use PCA for dimensionality reduction. You will also practice regularization, error analysis, and deployment checks, gaining skills you can apply immediately to real data projects.

Elevify Differentials

Develop Skills

  • Build feature matrices: turn BI tables into clean X and y for modeling fast.
  • Model with matrices: express, fit, and interpret linear models for BI KPIs.
  • Detect multicollinearity: use XᵀX, VIF, and SVD to find redundant BI features.
  • Apply PCA in BI: reduce dimensions, stabilize models, and speed up training.
  • Monitor models in production: track drift, retrain, and explain results to stakeholders.

Suggested Summary

Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.
Workload: between 4 and 360 hours

What our students say

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