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#

CommandDescriptionml-roadmap draft <input>Draft a new ML learning plan or content entryml-roadmap edit <input>Edit an existing entry or refine contentml-roadmap optimize <input>Optimize content for clarity or effectivenessml-roadmap schedule <input>Schedule learning sessions or content publicationml-roadmap hashtags <input>Generate relevant hashtags for ML topicsml-roadmap hooks <input>Create engaging hooks for ML contentml-roadmap cta <input>Generate call-to-action text for ML resourcesml-roadmap rewrite <input>Rewrite content with improved structureml-roadmap translate <input>Translate ML content between languagesml-roadmap tone <input>Adjust the tone of ML content (formal, casual, etc.)ml-roadmap headline <input>Generate compelling headlines for ML topicsml-roadmap outline <input>Create structured outlines for ML subjectsml-roadmap statsShow summary statistics across all entry typesml-roadmap export <fmt>Export all data (formats: json, csv, txt)ml-roadmap search <term>Search across all entries by keywordml-roadmap recentShow the 20 most recent activity log entriesml-roadmap statusHealth check — version, disk usage, last activityml-roadmap helpShow the built-in help messageml-roadmap versionPrint 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#

1. Planning your ML learning path — use outline and draft to structure a study roadmap covering supervised learning, deep learning, NLP, computer vision, and more
2. Creating ML educational content — use headline, hooks, cta, and hashtags to craft engaging posts or articles about machine learning concepts
3. Scheduling study sessions — use schedule to log when you plan to study specific ML topics and track your progress over time
4. Refining technical writing — use rewrite, tone, and optimize to polish ML blog posts, documentation, or course materials
5. Tracking content creation history — use stats, search, and recent to review what you've written, find past entries, and measure productivity

Examples#

bash

# 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:

bash

ml-roadmap stats > roadmap-report.txt
ml-roadmap export csv

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