Machine Learning Roadmap

Follow a structured ML roadmap connecting concepts, tools, and learning resources. Use when planning study paths, discovering resources, mapping skills.

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 .log file (e.g., draft.log, headline.log, translate.log)
• Entries are timestamped in YYYY-MM-DD HH:MM|<value> format
• A unified history.log tracks all actions across command types
• Export files are written to the same directory as export.json, export.csv, or export.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

1. Drafting ML content — use draft and outline to capture ideas and structure articles, blog posts, or course materials about machine learning topics
2. Headline and hook creation — record headline and hooks entries to brainstorm attention-grabbing titles and opening lines for ML content
3. Content optimization — use optimize, rewrite, and tone to track iterations as you refine ML tutorials, documentation, or marketing copy
4. Multi-language content — record translate entries when adapting ML learning materials for different language audiences
5. Content scheduling and CTAs — use schedule and cta to 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 stats

Output

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.txt

Configuration

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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