“machine learning”

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

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

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.

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.

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

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

Master ML operations best practices in Arabic

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

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

Supports machine learning in Python with scikit-learn.

Machine learning with scikit-learn.

Apple HIG guidance for Apple technology integrations: Siri, Apple Pay, HealthKit, HomeKit, ARKit, machine learning, generative AI, iCloud, Sign in with Apple…

Fine-tune MACE machine learning interatomic potentials on custom datasets.

Fine-tune MatGL machine learning interatomic potentials on custom datasets.

Machine learning: clustering, PCA/t-SNE/UMAP, classification, prediction regression (Ridge/Lasso/ensemble), cross-validation, Pipelines.

Detects and prevents data leakage in machine learning and mathematical modeling.