Data Science & ML Claude 스킬

Data Anomaly Detection

Identifies outliers and unusual patterns in datasets using specialized machine learning algorithms like Isolation Forest and One-Class SVM.

Data Anomaly & Outlier Detection

Identifies unusual patterns and outliers in datasets using advanced machine learning algorithms to uncover potential fraud, errors, or security threats.

AutoML Pipeline Builder

Generates production-grade automated machine learning pipelines including model selection, hyperparameter tuning, and performance evaluation.

AutoML Pipeline Builder

Automates the creation of machine learning pipelines including feature engineering, model selection, and hyperparameter tuning.

Classification Model Builder

Automates the end-to-end process of building, training, and evaluating robust classification models for supervised learning tasks.

ML Classification Builder

Builds, optimizes, and evaluates robust supervised learning classification models from structured datasets using automated pipelines.

Clustering Algorithm Runner

Executes advanced machine learning clustering algorithms to identify patterns, group data points, and perform unsupervised learning analysis.

Clustering Analysis Runner

Executes and visualizes diverse clustering algorithms to identify patterns and groupings within complex datasets automatically.

Computer Vision Adapter

Analyzes and processes images using advanced computer vision techniques like object detection, classification, and segmentation.

Computer Vision Processor

Automates complex image analysis tasks including object detection, classification, and segmentation directly within the Claude Code environment.

Data Preprocessing & ETL Pipelines

Automates the creation and execution of data cleaning, validation, and transformation pipelines for machine learning workflows.

Automated Data Preprocessing Pipeline

Builds and executes automated data cleaning, transformation, and ETL pipelines to prepare datasets for machine learning models.

ML Dataset Partitioning Adapter

Automates the partitioning of datasets into training, validation, and testing sets for machine learning workflows.

Dataset Splitting & Partitioning

Automates the partitioning of datasets into training, validation, and testing sets to streamline machine learning model development.

Data Visualization Creator

Generates professional-grade data visualizations, plots, and charts automatically from raw datasets using intelligent analysis.

Data Visualization Creator

Generates professional charts, plots, and graphs by automatically analyzing datasets to determine the most effective visual representation.

Deep Learning Model Optimizer

Optimizes deep learning models by automatically adjusting architectures, learning rates, and training parameters to improve performance and efficiency.

Deep Learning Model Optimizer

Optimizes deep learning model performance by refining architectures, tuning hyperparameters, and accelerating training workflows.

ML Experiment Tracking Adapter

Automates the configuration and initialization of experiment tracking tools like MLflow and Weights & Biases for machine learning workflows.

ML Experiment Tracking Setup

Automates the configuration and integration of MLflow and Weights & Biases for machine learning experiment management.

Machine Learning Feature Engineering

Automates feature creation, selection, and transformation to optimize machine learning model performance and data preparation.

Machine Learning Feature Engineering

Automates the creation, selection, and transformation of data features to optimize machine learning model performance and accuracy.

Hyperparameter Tuning & Optimization

Automates the discovery of optimal machine learning model configurations using grid search, random search, and Bayesian optimization.

ML Hyperparameter Optimizer

Optimizes machine learning models using automated grid, random, and Bayesian search strategies to maximize predictive performance.

Automated ML Model Trainer

Automates the machine learning lifecycle by analyzing datasets, selecting optimal algorithms, and training models with cross-validation.

Automated ML Training

Automates the end-to-end machine learning workflow by analyzing datasets, selecting optimal model types, and generating production-ready model artifacts.

Machine Learning Model Deployment Adapter

Automates the transition of machine learning models from development to production environments through API generation and containerization.

ML Model Deployment Adapter

Automates the deployment and serving of machine learning models into production environments using industry best practices.

Machine Learning Model Evaluation

Evaluates machine learning model performance using a comprehensive suite of metrics to facilitate data-driven optimization and validation.

Machine Learning Model Evaluator

Evaluates machine learning model performance using a comprehensive suite of metrics to ensure accuracy and reliability.