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Logistic Regression Course

Logistic Regression Course
from 4 to 360h flexible workload
valid certificate in your country

What will I learn?

Gain expertise in logistic regression through this practical course, progressing from data preparation to deployable models. Cover preprocessing, feature creation, model building, regularization techniques, and thorough assessments using ROC, AUC, and calibration metrics. Interpret results for business value, implement A/B testing, track model drift, and develop clear scoring systems.

Elevify advantages

Develop skills

  • Develop strong logistic models from exploratory analysis to optimised, regularised versions.
  • Create effective features through encoding, scaling, and handling class imbalances to improve performance.
  • Translate model coefficients and odds ratios into practical business insights swiftly.
  • Assess models thoroughly with ROC curves, precision-recall, calibration, cross-validation, and confidence intervals.
  • Implement model deployment with scoring pipelines, drift detection, and retraining strategies.

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 got 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 extra videos really help with learning.
Giulio Carlo
Giulio CarloPrompt Engineering Student

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