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introduction To Machine Learning for Data Science Course

introduction To Machine Learning for Data Science Course
from 4 to 360h flexible workload
valid certificate in your country

What will I learn?

This short, practical Introduction to Machine Learning for Data Science Course walks you step by step through framing a churn prediction problem, exploring and cleaning real subscription app data, and building reliable models. You will learn feature engineering, model selection, evaluation, interpretation, and production deployment so you can turn raw data into measurable retention and revenue impact fast.

Elevify advantages

Develop skills

  • Frame ML churn problems: turn tech KPIs into clear, actionable objectives.
  • Run fast EDA: clean, profile, and validate subscription app datasets.
  • Build and tune churn models: logistic, trees, ensembles with the right metrics.
  • Engineer powerful features: balance classes, encode categories, scale numerics.
  • Interpret and deploy models: explain SHAP, monitor drift, drive retention.

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 classes 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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