- name
- Machine Learning Roadmap
- description
- Follow a structured ML roadmap connecting concepts, tools, and learning resources. Use when planning study paths, discovering resources, mapping skills.
- version
- 1.0.0
- license
- MIT
- runtime
- python3
Machine Learning Roadmap
Machine Learning Roadmap v2.0.0 — a content toolkit for drafting, editing, optimizing, and managing machine learning content. Create outlines, write headlines, generate CTAs, manage hashtags, rewrite content, translate text, and adjust tone — all tracked with timestamped entries stored locally.
Commands
Run scripts/script.sh <command> [args] to use.
| Command | Description |
|---|---|
draft <input> | Record a draft entry. Without args, shows the 20 most recent draft entries. |
edit <input> | Record an edit entry. Without args, shows recent edit entries. |
optimize <input> | Record an optimization entry. Without args, shows recent optimize entries. |
schedule <input> | Record a scheduling entry. Without args, shows recent schedule entries. |
hashtags <input> | Record a hashtags entry. Without args, shows recent hashtags entries. |
hooks <input> | Record a hooks entry. Without args, shows recent hooks entries. |
cta <input> | Record a call-to-action entry. Without args, shows recent CTA entries. |
rewrite <input> | Record a rewrite entry. Without args, shows recent rewrite entries. |
translate <input> | Record a translation entry. Without args, shows recent translate entries. |
tone <input> | Record a tone adjustment entry. Without args, shows recent tone entries. |
headline <input> | Record a headline entry. Without args, shows recent headline entries. |
outline <input> | Record an outline entry. Without args, shows recent outline entries. |
stats | Show summary statistics across all entry types (counts, data size). |
export <fmt> | Export all data in json, csv, or txt format. |
search <term> | Search all log files for a term (case-insensitive). |
recent | Show the 20 most recent entries from the activity history. |
status | Health check — version, data directory, entry count, disk usage. |
help | Show help message with all available commands. |
version | Show version string (machine-learning-roadmap v2.0.0). |
Data Storage
All data is stored in ~/.local/share/machine-learning-roadmap/:
- Each command type writes to its own
.logfile (e.g.,draft.log,headline.log,translate.log) - Entries are timestamped in
YYYY-MM-DD HH:MM|<value>format - A unified
history.logtracks all actions across command types - Export files are written to the same directory as
export.json,export.csv, orexport.txt
Requirements
- Bash 4+ with
set -euo pipefail - Standard Unix utilities (
date,wc,du,tail,grep,sed,cat) - No external dependencies — works out of the box on Linux and macOS
When to Use
- Drafting ML content — use
draftandoutlineto capture ideas and structure articles, blog posts, or course materials about machine learning topics - Headline and hook creation — record
headlineandhooksentries to brainstorm attention-grabbing titles and opening lines for ML content - Content optimization — use
optimize,rewrite, andtoneto track iterations as you refine ML tutorials, documentation, or marketing copy - Multi-language content — record
translateentries when adapting ML learning materials for different language audiences - Content scheduling and CTAs — use
scheduleandctato plan publication timelines and track call-to-action variations for ML courses or newsletters
Examples
# Draft a new ML blog post idea
machine-learning-roadmap draft "Introduction to Neural Networks: A Beginner's Guide"
# Create an outline for a tutorial
machine-learning-roadmap outline "1. What is ML? 2. Supervised vs Unsupervised 3. Tools 4. Practice Projects"
# Record a headline variation
machine-learning-roadmap headline "5 Python Libraries Every ML Engineer Must Know in 2025"
# Generate hashtags for social media
machine-learning-roadmap hashtags "#MachineLearning #AI #DeepLearning #Python #DataScience"
# Export all content data as CSV
machine-learning-roadmap export csv
# Search for entries mentioning a topic
machine-learning-roadmap search "neural"
# View summary statistics
machine-learning-roadmap statsOutput
All commands print results to stdout. Each recording command confirms the save and shows the total entry count for that category. Redirect output to a file with:
machine-learning-roadmap stats > report.txtConfiguration
Set the DATA_DIR inside the script or modify the default path ~/.local/share/machine-learning-roadmap/ to change where data is stored.
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