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Linear Algebra for Data Science Course

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

What will I learn?

This Linear Algebra for Data Science Course equips you with practical tools to construct, comprehend, and enhance predictive models. You will learn to build feature matrices, implement linear models, analyse weights, spot redundancy, and employ PCA for dimensionality reduction. Additionally, you will engage in regularization, error assessment, and deployment verification, acquiring skills applicable right away to actual data initiatives.

Elevify advantages

Develop skills

  • Build feature matrices: convert BI tables into neat X and y for swift modelling.
  • Model with matrices: formulate, fit, and analyse linear models for BI KPIs.
  • Detect multicollinearity: utilise XᵀX, VIF, and SVD to identify redundant BI features.
  • Apply PCA in BI: diminish dimensions, steady models, and accelerate training.
  • Monitor models in production: observe drift, retrain, and clarify 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

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