Machine Learning — full-lifecycle ML development patterns (short listing)
Short listing — license not verifiable: problem definition and metrics by task type, data preparation and feature engineering, model selection, hyperparameter tuning, evaluation, production deployment, monitoring, and MLOps practices
- What
- Short listing — license not verifiable: problem definition and metrics by task type, data preparation and feature engineering, model selection, hyperparameter tuning, evaluation, production deployment, monitoring, and MLOps practices
- Cost
- Free
- Needs
- an ML problem to work on and your own training data — the skill is a guidance playbook, so you bring the datasets, compute, and ML frameworks (scikit-learn, XGBoost, PyTorch, etc.)
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Selected by Skill Harbor — short listing (neither the repo frontmatter nor the discovery manifest carries a verifiable license — the manifest records null, so no content is reproduced): @89jobrien's machine-learning skill, listed here with credit to its creator — a complete ML development lifecycle reference. It covers problem definition (classification, regression, clustering, ranking, anomaly detection) with success metrics per task type, data preparation (missing values, outliers, leakage detection), feature engineering patterns (numerical, categorical, temporal, text, geospatial), algorithm selection guides by data size, hyperparameter tuning methods, evaluation best practices, handling imbalanced data, production deployment patterns (REST API, batch, streaming, edge), monitoring and retraining triggers, plus MLOps practices — experiment tracking, model versioning, and CI/CD for ML. Honest caveats: license terms not verifiable — short listing with a link only, nothing copied; a guidance playbook, not runnable code for your data — you still need your own datasets and training infrastructure; framework versions drift, so check current docs for exact APIs; follow the source link and read the terms yourself before use. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: an ML problem to work on and your own training data — the skill is a guidance playbook, so you bring the datasets, compute, and ML frameworks (scikit-learn, XGBoost, PyTorch, etc.) Install "Machine Learning — full-lifecycle ML development patterns (short listing)" for me. It gives my agent @89jobrien's ML lifecycle reference: problem definition with metrics per task type, data preparation and feature engineering patterns, algorithm selection by data size, hyperparameter tuning methods, evaluation best practices, production deployment patterns, monitoring and retraining triggers, and MLOps practices (experiment tracking, model versioning, CI/CD). IMPORTANT: the license terms were not verifiable (the discovery manifest records no license) — fetch from the link only, reproduce nothing beyond the link, and read the terms yourself before use. Repository: https://github.com/89jobrien/steve/blob/main/steve/skills/machine-learning/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "machine-learning". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. provide my own training data and compute; adapt the patterns to the ML frameworks I actually use; read the license terms before reuse). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.