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Linear Algebra for Data Science Course

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

What will I learn?

Acquire essential linear algebra skills for data science through this course. Master building feature matrices, implementing linear models, interpreting coefficients, spotting multicollinearity, and leveraging PCA for efficient dimensionality reduction. Additionally, explore regularization methods, error assessment, and model deployment strategies applicable to real-world data initiatives.

Elevify advantages

Develop skills

  • Build feature matrices from data tables for quick modelling.
  • Apply matrix operations to fit and interpret linear models.
  • Identify multicollinearity using VIF and SVD techniques.
  • Implement PCA to reduce dimensions and enhance model stability.
  • Monitor production models for drift and stakeholder reporting.

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

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