Scikit Learn Mastery

source : zero-to-mastery https://github.com/mrdbourke/zero-to-mastery-ml

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A practical guide to learning machine learning with Python for beginners and intermediates. Covers 16 lessons: data loading, preprocessing, feature selection, and more. Includes 3 projects: Iris, Bost

Comprehensive Python exercises progressing from intermediate fundamentals through OOP, data structures, file handling, and data science/ML implementations. Covers real-world applications with NumPy, P

Workflow de bout en bout des librairies Python pour le Machine Learning (Jupyter/Pandas/Numpy/Matplotlib/Scikit-Learn) à l'aide de la formation ZERO TO MASTERY : DATASCIENCE BOOTCAMP 2021

Nine diagnostic tools for detecting and understanding overfitting in scikit-learn models — polynomial overfitting, learning curves, validation curves, bias-variance decomposition, regularisation sweep

A world-class expert would master machine learning by deeply understanding algorithms and implementing them from scratch without using high-level libraries like TensorFlow, PyTorch, or Scikit-learn. H

This repository features my hands-on Scikit-learn journey—from core algorithms to production-ready ML pipelines, perfect for data science and AI engineering portfolios.