Ml Roadmap

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 | Draft a new ML learning plan or content entry | | ml-roadmap edit | Edit an existing entry or refine content | | ml-roadmap optimize | Optimize content for clarity or effectiveness | | ml-roadmap schedule | Schedule learning sessions or content publication | | ml-roadmap hashtags | Generate relevant hashtags for ML topics | | ml-roadmap hooks | Create engaging hooks for ML content | | ml-roadmap cta | Generate call-to-action text for ML resources | | ml-roadmap rewrite | Rewrite content with improved structure | | ml-roadmap translate | Translate ML content between languages | | ml-roadmap tone | Adjust the tone of ML content (formal, casual, etc.) | | ml-roadmap headline | Generate compelling headlines for ML topics | | ml-roadmap outline | Create structured outlines for ML subjects | | ml-roadmap stats | Show summary statistics across all entry types | | ml-roadmap export | Export all data (formats: json, csv, txt) | | ml-roadmap search | 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:

Data Storage

All data is stored as plain-text log files in ~/.local/share/ml-roadmap/:

Set the ML_ROADMAP_DIR environment variable to override the default data directory.

Requirements

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

# 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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# 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