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Linear Algebra for Data Science Course

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

What will I learn?

This Linear Algebra for Data Science Course provides you with 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 practise regularisation, error analysis, and deployment checks, gaining skills you can apply immediately to real data projects.

Elevify advantages

Develop skills

  • Build feature matrices: turn BI tables into clean X and y for modelling 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, stabilise 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 the 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

Your lessons are perfect. I purchased the one-year package and finally have the chance to follow several topics I'm interested in without having to switch platforms... thank you for everything you do, I've already recommended you to others...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons get straight to the point and how I can switch chapters and skip content I don't need.
Mariana Ferres
Mariana FerresPhotography Student
I like the content and the way the videos are presented and transcribed, which speeds up the process!
Luciana Alvarenga
Luciana AlvarengaNail Design Student
The platform is fast and easy to use. The variety of content and the supplementary videos really help with learning.
Giulio Carlo
Giulio CarloPrompt Engineering Student

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