No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by mikeroyal · Agent Tool · ★ 708
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Machine-Learning-Guide safe to install? View the security audit →
Machine Learning Guide A guide covering Machine Learning including the applications, libraries and tools that will make you better and more efficient with Machine Learning development. Note: You can easily convert this markdown file to a PDF in VSCode using this handy extension Markdown PDF. Machine Learning/Deep Learning Frameworks. Table of Contents Learning Resources for ML Developer Resources [Courses & Certifications](https://github.com/
| Stars | 708 |
| Forks | 65 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 50.5598168575889/100 |
| Open Issues | 2 |
| Last Updated | 2024-01-04 |
| Created | 2020-10-17 |
| Platforms | aws, python |
| Est. Tokens | ~71k |
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Machine-Learning-Guide is Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, Large Language Models (LLMs), and Training Models.. It is categorized as a Agent Tool with 708 GitHub stars.
Machine-Learning-Guide is primarily written in Python. It covers topics such as artificial-neural-networks, aws-sagemaker, deep-learning.
You can find installation instructions and usage details in the Machine-Learning-Guide GitHub repository at github.com/mikeroyal/Machine-Learning-Guide. The project has 708 stars and 65 forks, indicating an active community.
The top alternatives to Machine-Learning-Guide on Agent Skills Hub include prompttools, awesome-llm-powered-agent, DemoGPT. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: