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Machine Learning

aiskillstore/marketplace

  • 3 installs
  • 404 repo stars
  • Updated August 5, 2026
  • aiskillstore/marketplace

This is a copy of machine-learning by 89jobrien - installs and ranking accrue to the original listing.

machine-learning is a Claude Code skill covering the full ML lifecycle, from feature engineering and model training to deployment and monitoring.

About

machine-learning is a Claude Code skill covering the full machine-learning lifecycle from data preparation to production deployment. A developer uses it for feature engineering, model selection, hyperparameter tuning, evaluation, and MLOps concerns like experiment tracking, model versioning and drift monitoring. It provides algorithm-selection guides, metric tables and serving patterns.

  • Covers the full ML lifecycle from experimentation to production
  • Feature engineering, model selection and hyperparameter tuning guidance
  • MLOps: experiment tracking, model versioning and drift monitoring

Machine Learning by the numbers

  • 3 all-time installs (skills.sh)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
At a glance

machine-learning capabilities & compatibility

Capabilities
feature engineering · model selection · hyperparameter tuning · model evaluation · ml deployment
Use cases
data analysis
From the docs

What machine-learning says it does

Comprehensive machine learning skill covering the full ML lifecycle from experimentation to production deployment.
SKILL.md
Resampling: SMOTE, undersampling
SKILL.md
npx skills add https://github.com/aiskillstore/marketplace --skill machine-learning

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Listed on Skillselion
Installs3
repo stars★ 404
Last updatedAugust 5, 2026
Repositoryaiskillstore/marketplace ↗

What it does

Build and deploy an ML pipeline with feature engineering, model selection and MLOps monitoring.

Who is it for?

Building ML pipelines and taking models from experimentation to production

When should I use this skill?

Building ML pipelines, training models, feature engineering, or deploying ML systems

What you get

  • ML pipeline
  • trained model
  • evaluation report

By the numbers

  • 7-step ML development lifecycle
  • algorithm selection guide across 3 data-size tiers
  • 3 reference files (preprocessing, model patterns, evaluation)

Files

SKILL.mdMarkdownGitHub ↗

Machine Learning

Comprehensive machine learning skill covering the full ML lifecycle from experimentation to production deployment.

When to Use This Skill

  • Building machine learning pipelines
  • Feature engineering and data preprocessing
  • Model training, evaluation, and selection
  • Hyperparameter tuning and optimization
  • Model deployment and serving
  • ML experiment tracking and versioning
  • Production ML monitoring and maintenance

ML Development Lifecycle

1. Problem Definition

Classification Types:

  • Binary classification (spam/not spam)
  • Multi-class classification (image categories)
  • Multi-label classification (document tags)
  • Regression (price prediction)
  • Clustering (customer segmentation)
  • Ranking (search results)
  • Anomaly detection (fraud detection)

Success Metrics by Problem Type:

Problem TypePrimary MetricsSecondary Metrics
Binary ClassificationAUC-ROC, F1Precision, Recall, PR-AUC
Multi-classMacro F1, AccuracyPer-class metrics
RegressionRMSE, MAER², MAPE
RankingNDCG, MAPMRR
ClusteringSilhouette, Calinski-HarabaszDavies-Bouldin

2. Data Preparation

Data Quality Checks:

  • Missing value analysis and imputation strategies
  • Outlier detection and handling
  • Data type validation
  • Distribution analysis
  • Target leakage detection

Feature Engineering Patterns:

  • Numerical: scaling, binning, log transforms, polynomial features
  • Categorical: one-hot, target encoding, frequency encoding, embeddings
  • Temporal: lag features, rolling statistics, cyclical encoding
  • Text: TF-IDF, word embeddings, transformer embeddings
  • Geospatial: distance features, clustering, grid encoding

Train/Test Split Strategies:

  • Random split (standard)
  • Stratified split (imbalanced classes)
  • Time-based split (temporal data)
  • Group split (prevent data leakage)
  • K-fold cross-validation

3. Model Selection

Algorithm Selection Guide:

Data SizeProblemRecommended Models
Small (<10K)ClassificationLogistic Regression, SVM, Random Forest
Small (<10K)RegressionLinear Regression, Ridge, SVR
Medium (10K-1M)ClassificationXGBoost, LightGBM, Neural Networks
Medium (10K-1M)RegressionXGBoost, LightGBM, Neural Networks
Large (>1M)AnyDeep Learning, Distributed training
TabularAnyGradient Boosting (XGBoost, LightGBM, CatBoost)
ImagesClassificationCNN, ResNet, EfficientNet, Vision Transformers
TextNLPTransformers (BERT, RoBERTa, GPT)
SequentialTime SeriesLSTM, Transformer, Prophet

4. Model Training

Hyperparameter Tuning:

  • Grid Search: exhaustive, good for small spaces
  • Random Search: efficient, good for large spaces
  • Bayesian Optimization: smart exploration (Optuna, Hyperopt)
  • Early stopping: prevent overfitting

Common Hyperparameters:

ModelKey Parameters
XGBoostlearning_rate, max_depth, n_estimators, subsample
LightGBMnum_leaves, learning_rate, n_estimators, feature_fraction
Random Forestn_estimators, max_depth, min_samples_split
Neural Networkslearning_rate, batch_size, layers, dropout

5. Model Evaluation

Evaluation Best Practices:

  • Always use held-out test set for final evaluation
  • Use cross-validation during development
  • Check for overfitting (train vs validation gap)
  • Evaluate on multiple metrics
  • Analyze errors qualitatively

Handling Imbalanced Data:

  • Resampling: SMOTE, undersampling
  • Class weights: weighted loss functions
  • Threshold tuning: optimize decision threshold
  • Evaluation: use PR-AUC over ROC-AUC

6. Production Deployment

Model Serving Patterns:

  • REST API (Flask, FastAPI, TF Serving)
  • Batch inference (scheduled jobs)
  • Streaming (real-time predictions)
  • Edge deployment (mobile, IoT)

Production Considerations:

  • Latency requirements (p50, p95, p99)
  • Throughput (requests per second)
  • Model size and memory footprint
  • Fallback strategies
  • A/B testing framework

7. Monitoring & Maintenance

What to Monitor:

  • Prediction latency
  • Input feature distributions (data drift)
  • Prediction distributions (concept drift)
  • Model performance metrics
  • Error rates and types

Retraining Triggers:

  • Performance degradation below threshold
  • Significant data drift detected
  • Scheduled retraining (daily, weekly)
  • New training data available

MLOps Best Practices

Experiment Tracking

Track for every experiment:

  • Code version (git commit)
  • Data version (hash or version ID)
  • Hyperparameters
  • Metrics (train, validation, test)
  • Model artifacts
  • Environment (packages, versions)

Model Versioning

models/
├── model_v1.0.0/
│   ├── model.pkl
│   ├── metadata.json
│   ├── requirements.txt
│   └── metrics.json
├── model_v1.1.0/
└── model_v2.0.0/

CI/CD for ML

1. Continuous Integration:

  • Data validation tests
  • Model training tests
  • Performance regression tests

2. Continuous Deployment:

  • Staging environment validation
  • Shadow mode testing
  • Gradual rollout (canary)
  • Automatic rollback

Reference Files

For detailed patterns and code examples, load reference files as needed:

  • `references/preprocessing.md` - Data preprocessing patterns and feature engineering techniques
  • `references/model_patterns.md` - Model architecture patterns and implementation examples
  • `references/evaluation.md` - Comprehensive evaluation strategies and metrics

Integration with Other Skills

  • performance - For optimizing inference latency
  • testing - For ML-specific testing patterns
  • database-optimization - For feature store queries
  • debugging - For model debugging and error analysis

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