Machine Learning Roadmap

---

version: "2.0.0"

name: Machine Learning Roadmap

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

---

# 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:

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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