cerlymarco/MEDIUM_NoteBook resource

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2144 stars · 964 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks accompanying the author's Medium posts on machine learning and data science topics. It covers practical tutorials on time series forecasting, explainability, causal inference, and statistical methods with ML approaches.

Use cases

• learn time series forecasting techniques with sklearn
• understand SHAP explainability with proxy models
• study causal inference with synthetic control and ML
• explore hypothesis testing with machine learning approaches
• learn PCA for multivariate time series forecasting
• find example notebooks for imbalanced data model selection

When to choose

• you want hands-on notebook examples accompanying in-depth Medium tutorials
• you are learning applied ML topics like forecasting, explainability, and causal inference
• you want reusable code snippets for data science experiments

When to avoid

• you need a production-ready library or package
• you want a single cohesive tool rather than independent tutorial notebooks
• you need guaranteed maintenance or support

Facets

learning-resource · maturity active

machine-learning deep-learning data-science nlp machine-learning data-science tutorials artificial-intelligence python jupyter-notebooks medium-posts time-series-forecasting explainability causal-inference educational

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