I’m building a free bilingual machine-learning notebook course — looking for feedback on structure and coverage [R]

Hi everyone,

I’m building an open-source machine-learning tutorial repository in Jupyter Notebook format:

https://github.com/mohammadijoo/Machine_Learning_Tutorials

The course is bilingual: English and Persian/Farsi versions are organized in parallel. The goal is to make a practical, notebook-first ML curriculum that students can run locally and study step by step.

Current focus areas include:

• ML foundations and workflow
• data cleaning, preprocessing, feature engineering
• regression and classification
• tree models and ensembles
• clustering and dimensionality reduction
• evaluation, cross-validation, calibration
• time series, anomaly detection, responsible ML, and MLOps concepts
• datasets and exercises for hands-on practice

I would appreciate feedback on:

• whether the chapter order makes sense for beginners
• what important classical ML topics are missing
• whether bilingual notebooks are useful for non-native English learners
• how to make the notebooks more practical without turning them into only “copy/paste code”

I’m sharing this as a free educational resource and would value constructive criticism.