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Machine Learning in Finance Course

Machine Learning in Finance Course
from 4 to 360h flexible workload
valid certificate in your country

What will I learn?

Master practical machine learning for credit risk, trading strategies, and fraud detection with this concise, high-impact course. Learn data preprocessing, feature engineering, labelling, and robust model design, then move to backtesting, deployment, monitoring, and governance. Gain skills in explainability, fairness, and compliance so you can build reliable, scalable, and audit-ready decision systems end to end.

Elevify advantages

Develop skills

  • Build ML credit risk models: from raw loan data to calibrated PD scores.
  • Design fraud detection pipelines: streaming features, drift control, and KPIs.
  • Create and backtest ML trading signals with robust, leak-free time series data.
  • Operationalise ML in finance: deployment, monitoring, governance, and audits.
  • Apply fair, explainable ML: bias checks, SHAP insights, and regulatory alignment.

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 purchased the one-year package and finally have the opportunity to follow various topics of interest without needing to change platforms... I thank you for everything you do, I've already recommended you to other people...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can change chapters and skip content I don't need.
Mariana Ferres
Mariana FerresPhotography Student
I like the content and the way videos are presented and transcribed, which speeds up the process!
Luciana Alvarenga
Luciana AlvarengaNail Design Student
The platform is fast, simple to use. The diversity of content and complementary videos really help with learning.
André Felipe
André FelipePrompt Engineering Student

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