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Machine Learning Roadmap

by ckchzh

A roadmap connecting many of the most important concepts in machine learning, how to learn them and machine learning roadmap, python, data, data-science.

View Chinese version with editor review

安装

claude skill add --url github.com/openclaw/skills/tree/main/skills/ckchzh/ml-roadmap

文档

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

CommandDescription
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 statsShow 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 recentShow the 20 most recent activity log entries
ml-roadmap statusHealth check — version, disk usage, last activity
ml-roadmap helpShow the built-in help message
ml-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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