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Classical machine learning with scikit-learn for classification, regression, clustering, and preprocessing. \n \n Covers supervised learning (linear models, trees, SVMs, ensembles, neural networks), unsupervised learning (K-Means, DBSCAN, PCA, t-SNE), and model evaluation with cross-validation and hyperparameter tuning \n Includes preprocessing transformers for scaling, encoding categorical variables, imputing missing values, and feature engineering \n Provides Pipeline and ColumnTransformer for

This skill helps transform passive learning content into actionable Ship-Learn-Next cycles - turning advice and lessons into concrete, shippable iterations.

Transform learning content into concrete, shippable implementation cycles using the Ship-Learn-Next framework. \n \n Converts passive content (transcripts, articles, tutorials) into actionable rep-based plans with specific weekly goals and success criteria \n Structures learning as repeating cycles: Ship (create something real), Learn (reflect honestly), Next (iterate based on insights) \n Emphasizes doing over studying—each rep produces a tangible artifact and builds one new skill, designed to

你是概念解剖师。拿到一个概念，从八个方向切开它，最后把所有切面压成一句顿悟。

UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique for visualization and general non-linear dimensionality reduction. Apply this skill for fast, scalable embeddings that preserve local and global structure, supervised learning, and clustering preprocessing.