Comprehensive guide for Python-based machine learning and data science workflows including pandas/numpy data manipulation, scikit-learn model development, PyTorch/TensorFlow deep learning, feature engineering, model evaluation, and Jupyter notebook best practices. Use when building ML models, analyzing data, or working with data science libraries.