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Linear Algebra for Data Science Course

Linear Algebra for Data Science Course
flexible workload from 4 to 360h
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 business intelligence tables into neat X and y for swift modelling.
  • Model with matrices: formulate, fit, and analyse linear models for business intelligence key performance indicators.
  • Detect multicollinearity: utilise XᵀX, VIF, and SVD to identify redundant business intelligence features.
  • Apply PCA in business intelligence: 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 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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Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can switch chapters and skip content I don't need.
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Mariana FerresPhotography Student
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Giulio CarloPrompt Engineering Student

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