“machine learning”

Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML.

Apply rigorous, ambitious engineering judgment to machine-learning work.

Machine learning development patterns, model training, evaluation, and deployment.

First-principles ML: classical methods, optimization, Gaussian processes, deep learning, probabilistic inference, generative models, RL.

**[REQUIRED]** For **ALL** data science and machine learning tasks.

Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits &…

Expert machine learning engineer skill.

Applies Tom M.

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries.

LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode.

Evaluate trained machine learning models with the right metrics and comparison logic.

面向大连理工大学机器学习课程的中文 skill。用于 Python notebook、numpy/pandas/matplotlib/scikit-learn、线性回归、逻辑回归、决策树、训练测试划分、评估指标、API 警告修复、作业检查和复习。

Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features.

Neo - Autonomous Machine Learning Engineer

Neo Research

Neo is an autonomous AI agent for machine learning engineers.

Knowledge base from 'Machine Learning for Asset Managers' by Marcos M.

Knowledge base from 'Advances in Financial Machine Learning' by Marcos M.

Maps query single-cell data to reference atlases using scArches transfer learning with scVI and scANVI models.

Master ML operations best practices in Arabic

Selects informative features for biomarker discovery using Boruta all-relevant selection, mRMR minimum redundancy, and LASSO regularization.

Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines.

Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.

Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

Machine learning in Python with scikit-learn.

This skill covers causal machine learning methods in applied economics and quantitative social science.