scikit-learn
What it is: Python's classical machine learning library. Random forests, regression, clustering, preprocessing—all with consistent API.
What It Does Best
Simple, consistent API. fit(), predict(), transform(). Same pattern for every algorithm.
Comprehensive toolkit. Classification, regression, clustering, preprocessing, model selection—everything non-deep-learning.
Production-ready. Stable, well-tested, used by thousands of companies.
Pricing
Free. Open source, BSD license.
When to Use It
✅ Tabular data (structured data in rows/columns)
✅ Traditional ML (not deep learning)
✅ Need fast prototyping
✅ Interpretable models
When NOT to Use It
❌ Images, text, audio (use PyTorch/TensorFlow)
❌ Deep neural networks (not supported)
❌ Very large datasets (memory limits)
Bottom line: Start here for ML. 80% of business problems don't need deep learning. scikit-learn handles them beautifully. Learn this before PyTorch.