Scikit-learn Classification Tutorial: Imbalanced Data, Pipelines & Explainability in Python скачать в хорошем качестве

Scikit-learn Classification Tutorial: Imbalanced Data, Pipelines & Explainability in Python

⚡Mark Attendance & Submit Assignment at One Place → https://luc.to/slcfeb26d1 Join Python Discussion group to get daily updates→ https://luc.to/pytwa In this advanced Scikit-learn workshop, you’ll build a solid end-to-end classification workflow in Python, focusing on real-world challenges beyond basic models. This is ideal for students and professionals who already know basic ML and want to go deeper with practical tools. You’ll learn how to handle imbalanced datasets, work with multiclass and multilabel classification, and build clean, reusable pipelines. We’ll cover cross-validation, model evaluation, feature importance, and model interpretability so you can understand and trust your predictions. We’ll also walk through real-world style case studies to see how these techniques apply in practice. By the end, you’ll have a stronger grasp of how to use Scikit-learn for robust, production-ready classification. Subscribe for more free ML workshops, mini projects, and Scikit-learn tutorials.