Using C# for Data Science and Machine Learning

on YouTube ↗27m

" summary

Shows a runnable Jupyter setup using .NET Interactive and Microsoft.Data.Analysis to load the UCI bank CSV, tokenizes and featurizes categorical string columns, concatenates numeric features (age, balance) and trains a logistic regression via ML.NET; performs cross‑validation and reports AUC ~90%. Builds PredictionEngine classes to output PredictedLabel and Score, runs example predictions (defaults vs nondefaults), and saves the trained model for deployment to Azure Blob or an API.

AI-written summary · about

" chapters (15)

1. 0:00Introduction to C# for ML
2. 1:08Setting up prerequisites
3. 2:10Installing .NET Interactive
4. 3:37Creating a C# notebook
5. 4:27Loading and exploring data
6. 7:21Analyzing job distribution
7. 9:51Visualizing job data
8. 11:40Examining marital status
9. 12:32Default loan analysis
10. 13:39Training ML model
11. 17:33Evaluating model performance
12. 19:03Making predictions
13. 23:29Deploying models
14. 24:59Conference announcement
15. 25:44Q&A and closing

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