Descubre Habilidades de Claude para data science & ml. Explora 61 habilidades y encuentra las capacidades perfectas para tus flujos de trabajo de IA.
Converts chemical structures into numerical representations for machine learning using over 100 specialized featurizers.
Builds and deploys production-ready generative AI agents leveraging Google Cloud's Vertex AI and Gemini models.
Automates the creation, selection, and transformation of data features to optimize machine learning model performance and interpretability.
Optimizes neural network performance by fine-tuning architectures, hyperparameters, and training schedules to maximize accuracy and efficiency.
Automates the end-to-end creation, training, and evaluation of machine learning models using sophisticated AutoML techniques.
Automates the tracking, management, and performance monitoring of machine learning model versions within your development workflow.
Manages and tracks AI/ML model versions, lineage, and performance metrics directly within your development environment.
Optimizes deep learning model performance, accuracy, and training efficiency through automated architectural analysis and advanced algorithm selection.
Automates the creation of complex Excel pivot tables and data visualizations using natural language commands.
Accesses and analyzes somatic mutation data from the world's largest cancer genomics database for precision oncology research.
Queries and retrieves global clinical study data from ClinicalTrials.gov via API v2 for medical research and patient matching.
Creates, edits, and analyzes Excel spreadsheets with production-grade formulas, formatting, and rigorous error validation.
Provides a comprehensive suite of tools for processing, normalizing, and visualizing high-throughput sequencing data like ChIP-seq and RNA-seq.
Automates systematic literature reviews for sociology and academic research using the OpenAlex API and structured screening workflows.
Generates high-quality speech audio locally on Apple Silicon using MLX acceleration and the Kokoro model.
Provides deep insights and interpretability for machine learning models using advanced techniques like SHAP and LIME.
Accesses the comprehensive BRENDA enzyme database to retrieve kinetic parameters, reaction equations, and biochemical data for metabolic research.
Analyzes historical time-series data to predict future values and identify temporal patterns like seasonality and trends.
Provides a specialized toolkit for applying machine learning to molecular property prediction, drug discovery, and materials science.
Provides rapid access to 20+ genomic databases and bioinformatics tools for gene searching, sequence alignment, and protein structure prediction.
Optimizes machine learning model performance by automatically searching for ideal hyperparameter configurations using advanced search strategies.
Automates the configuration and integration of MLflow and Weights & Biases to streamline machine learning experiment management.
Queries and analyzes over 240 million scholarly works, authors, and institutions via the OpenAlex API for comprehensive bibliometric research.
Conducts real-time academic research and technical documentation searches using Perplexity's Sonar models with automated citations.
Manages annotated data matrices and biological datasets using the AnnData framework in Python.
Automates the cleaning, transformation, and validation of raw data into high-quality datasets for machine learning and analysis.
Provides interpretability and transparency for machine learning models by explaining predictions and identifying key feature importance.
Accesses and retrieves nucleotide sequences, raw reads, and genomic metadata from the European Nucleotide Archive (ENA).
Simulates and analyzes closed and open quantum mechanical systems using the Quantum Toolbox in Python.
Builds professional-grade Leveraged Buyout (LBO) models in Excel for private equity analysis and transaction screening.
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