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 .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

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