This repository provides a curated collection of Jupyter notebooks and accompanying articles from Medium, focusing on advanced machine learning techniques for time series analysis, explainable AI, and predictive maintenance. It targets data scientists and ML engineers seeking practical implementations and insights into cutting-edge methodologies.

How It Works

The project showcases practical applications of various ML algorithms and concepts, including gradient boosting, conformal prediction, causal inference, and deep learning architectures like LSTMs and CNNs. Each notebook is linked to a corresponding Medium post, offering detailed explanations and code examples for implementing these techniques in real-world scenarios. The approach emphasizes bridging theoretical concepts with hands-on coding.

Quick Start & Requirements

• Notebooks are typically run using Python environments with libraries like Pandas, Scikit-learn, TensorFlow/Keras, and PyTorch.
• Specific requirements vary per notebook, often including GPU support for deep learning models.
• Links to associated Medium posts and code repositories are provided for each topic.

Highlighted Details

• Extensive coverage of time series forecasting techniques, including GenAI-inspired methods and probabilistic forecasting.
• In-depth exploration of Explainable AI (XAI) using SHAP for feature selection, drift detection, and model interpretation.
• Practical applications in predictive maintenance, anomaly detection, and survival analysis.
• Demonstrations of hybrid modeling approaches combining linear models with tree-based methods.

Maintenance & Community

The repository is maintained by cerlymarco, with links to associated Medium posts suggesting an active author. Further community engagement channels are not explicitly listed.

Licensing & Compatibility

The repository itself does not specify a license. The code within the notebooks is likely subject to the licenses of the libraries used (e.g., MIT for Scikit-learn, Apache 2.0 for TensorFlow). Compatibility for commercial use would depend on the specific licenses of the underlying libraries and any explicit licensing added to the repository.

Limitations & Caveats

The repository is a collection of individual posts and notebooks, lacking a unified project structure or overarching framework. Users may need to adapt code for specific environments, and the depth of explanation varies by the linked Medium article.