Machine Learning Roadmap
A thorough content toolkit for planning and tracking your machine learning learning journey. Draft study plans, organize topics, create outlines, schedule learning sessions, and manage your ML education roadmap — all from the command line.
Commands
| Command | Description |
|---|
ml-roadmap draft <input> | Draft a new ML learning plan or content entry |
ml-roadmap edit <input> | Edit an existing entry or refine content |
ml-roadmap optimize <input> | Optimize content for clarity or effectiveness |
ml-roadmap schedule <input> | Schedule learning sessions or content publication |
ml-roadmap hashtags <input> | Generate relevant hashtags for ML topics |
ml-roadmap hooks <input> | Create engaging hooks for ML content |
ml-roadmap cta <input> | Generate call-to-action text for ML resources |
ml-roadmap rewrite <input> | Rewrite content with improved structure |
ml-roadmap translate <input> | Translate ML content between languages |
ml-roadmap tone <input> | Adjust the tone of ML content (formal, casual, etc.) |
ml-roadmap headline <input> | Generate compelling headlines for ML topics |
ml-roadmap outline <input> | Create structured outlines for ML subjects |
ml-roadmap stats | Show summary statistics across all entry types |
ml-roadmap export <fmt> | Export all data (formats: json, csv, txt) |
ml-roadmap search <term> | Search across all entries by keyword |
ml-roadmap recent | Show the 20 most recent activity log entries |
ml-roadmap status | Health check — version, disk usage, last activity |
ml-roadmap help | Show the built-in help message |
ml-roadmap version | Print the current version (v2.0.0) |
Each content command (draft, edit, optimize, etc.) works in two modes:
- Without arguments — displays the 20 most recent entries of that type
- With arguments — saves the input as a new timestamped entry
Data Storage
All data is stored as plain-text log files in ~/.local/share/ml-roadmap/:
- Each command type gets its own log file (e.g.,
draft.log, edit.log, outline.log)
- Entries are stored in
timestamp|value format for easy parsing
- A unified
history.log tracks all activity across command types
- Export to JSON, CSV, or TXT at any time with the
export command
Set the ML_ROADMAP_DIR environment variable to override the default data directory.
Requirements
- Bash 4.0+ (uses
set -euo pipefail)
- Standard Unix utilities:
date, wc, du, tail, grep, sed, cat
- No external dependencies or API keys required
When to Use
- Planning your ML learning path — use
outline and draft to structure a study roadmap covering supervised learning, deep learning, NLP, computer vision, and more
- Creating ML educational content — use
headline, hooks, cta, and hashtags to craft engaging posts or articles about machine learning concepts
- Scheduling study sessions — use
schedule to log when you plan to study specific ML topics and track your progress over time
- Refining technical writing — use
rewrite, tone, and optimize to polish ML blog posts, documentation, or course materials
- Tracking content creation history — use
stats, search, and recent to review what you've written, find past entries, and measure productivity
Examples
# Draft a new learning plan for deep learning fundamentals
ml-roadmap draft "Week 1: Neural network basics — perceptrons, activation functions, backprop"
# Create an outline for a blog post on model selection
ml-roadmap outline "Comparing Random Forest vs XGBoost: when to use each, key hyperparameters, pros/cons"
# Generate a headline for an ML tutorial
ml-roadmap headline "Beginner-friendly guide to building your first image classifier with PyTorch"
# Schedule a study session
ml-roadmap schedule "Saturday 10am: Work through Stanford CS229 Lecture 5 — Support Vector Machines"
# Export all your entries to JSON for backup
ml-roadmap export json
Output
All commands print results to stdout. Redirect to a file if needed:
ml-roadmap stats > roadmap-report.txt
ml-roadmap export csv
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