Search: "ML"
Showing 22 of 22 results
classical-ml-drug-discovery
Build defensible molecular ML for drug discovery — QSAR, virtual screening, ADMET, toxicity, binding affinity, drug-target models — with Random Forest, SVM/SVR, and Gradient Boosting, plus dataset analysis, inductive conformal prediction, multi-model consensus, diversity-aware batch selection, and cheminformatics-aware interpretation. Use for curating bioactivity data, comparing RF/SVM/XGBoost, leakage-resistant validation, library screening, calibrated uncertainty, disagreeing-model reconciliation, diverse assay-batch selection, feature-to-molecule mapping, applicability-domain assessment, or auditing a molecular ML paper.
geoskill-lulc-classification-ml
多波段特征逐像元 RandomForest/梯度提升土地覆被分类，含精度评估、面积统计与众数滤波去盐噪
Ctf Ai Ml
Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, pro...
ML Pipeline
Complete machine learning pipeline for trading: feature engineering, AutoML, deep learning, and financial RL. Use for automated parameter sweeps, feature cre...
ML Engineering
Provides end-to-end methodology for defining, engineering, experimenting, deploying, and operating production ML/AI systems at scale.
bigquery-ai-ml
# BigQuery AI & ML BigQuery integrates with Vertex AI to provide powerful machine learning and generative AI capabilities directly within SQL queries using built-in functions like `AI.FORECAST`, `AI.KEY_DRIVERS`, `AI.DETECT_ANOMALIES`, and `AI.GENERATE`. ## Reference Directory - **Functions Reference**: - **AI.AGG**: [ai_agg.md](references/ai_agg.md) - Multi-row semantic aggregation and summarization. - **AI.CLASSIFY**: [ai_classify.md](references/ai_classify.md) - Classify text. - **AI.DETECT_ANOMALIES**:
ml-pipeline-workflow
# ML Pipeline Workflow Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. ## Overview This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. ## When to Use This Skill - Building new ML pipelines from scratch - Designing workflow orchestration for ML systems - Implementing data → model → deployment automation - Setting up reprod
agent-data-ml-model
--- name: "ml-developer" description: "Specialized agent for machine learning model development, training, and deployment" color: "purple" type: "data" version: "1.0.0" created: "2025-07-25" author: "Claude Code" metadata: specialization: "ML model creation, data preprocessing, model evaluation, deployment" complexity: "complex" autonomous: false # Requires approval for model deployment triggers: keywords: - "machine learning" - "ml model" - "train model" - "predict" - "classification" - "regression" - "neu
domain-ml
# Machine Learning Domain > **Layer 3: Domain Constraints** ## Domain Constraints → Design Implications | Domain Rule | Design Constraint | Rust Implication | |-------------|-------------------|------------------| | Large data | Efficient memory | Zero-copy, streaming | | GPU acceleration | CUDA/Metal support | candle, tch-rs | | Model portability | Standard formats | ONNX | | Batch processing | Throughput over latency | Batched inference | | Numerical precision | Float handling | ndarray, careful f32/f64 |
ml-ai
# Grafana Cloud AI & ML > **Docs**: https://grafana.com/docs/grafana-cloud/alerting-and-irm/machine-learning/ ML alerting + automated RCA + LLM-powered Assistant in one Grafana Cloud stack. ## Prerequisites - Grafana Cloud stack (Pro / Advanced — most features GA, some in preview) - API token with `plugins:write` for ML / Sift / LLM-plugin endpoints - For Dynamic Alerting: at least 14 days (ideally 90d) of history for the metric you want to forecast ## Common Workflows ### 1. Forecasting alert with Dynamic
ai-ml-security
# SKILL: AI/ML Security — Expert Attack Playbook > **AI LOAD INSTRUCTION**: Expert AI/ML security techniques. Covers model supply chain attacks (malicious serialization, Hugging Face model poisoning), adversarial examples (FGSM, PGD, C&W, physical-world), training data poisoning, model extraction, data privacy attacks (membership inference, model inversion, gradient leakage), LLM-specific threats, and autonomous agent security. Base models underestimate the severity of pickle deserialization RCE and the pra
Advanced ML Classification Skill
自动化生成工业级机器学习分类算法代码、调用算法做预测、输出准确率对比和可视化结果，支持新手友好的结果解读。
Ml Visualizer
Visual analysis and diagnostic tools to help machine learning model selection. ml-visualizer, python, anaconda, estimator, machine-learning, matplotlib.
ML Experiment Tracker
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment def...
ML Evolution Agent
Auto-evolving ML competition agent. Learns from each experiment, accumulates HCC multi-layer memory, and continuously improves LB scores. Inspired by MLE-Ben...
ml-engineer
Expert ML engineer specializing in machine learning model lifecycle, production deployment, and ML system optimization. Masters both traditional ML and deep...
Pilot Ml Training Pipeline Setup
Deploy an end-to-end ML training pipeline with 4 agents. Use this skill when: 1. User wants to set up a machine learning training pipeline 2. User is configu...
Advanced Financial Ml
MlFinLab 提供金融机器学习高级实现，包括信息驱动 bars（tick/volume/dollar/imbalance bars）、分数阶差分和回测工具，支持多市场因子研究与策略验证。
ML Training
Train, evaluate, tune, and deploy supervised, unsupervised, and transfer learning models using PyTorch, TensorFlow, and scikit-learn on Nautilus.
Ml Ops
Deep MLOps workflow—reproducible training, experiment tracking, packaging, deployment, monitoring (drift, performance), governance, and rollback for ML. Use...
AI ML Engineer
Generate personalized AI/ML engineering career roadmaps based on individual experience, skills, and goals.