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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1---2name: machine-learning-roadmap3description: A roadmap connecting many of the most important concepts in machine learning, how to learn them and machine learning roadmap, python, data, data-science.4---5
6# Machine Learning Roadmap
7
8A 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.
9
10## Commands
11
12| Command | Description |
13|---------|-------------|
14| `ml-roadmap draft <input>` | Draft a new ML learning plan or content entry |
15| `ml-roadmap edit <input>` | Edit an existing entry or refine content |
16| `ml-roadmap optimize <input>` | Optimize content for clarity or effectiveness |
17| `ml-roadmap schedule <input>` | Schedule learning sessions or content publication |
18| `ml-roadmap hashtags <input>` | Generate relevant hashtags for ML topics |
19| `ml-roadmap hooks <input>` | Create engaging hooks for ML content |
20| `ml-roadmap cta <input>` | Generate call-to-action text for ML resources |
21| `ml-roadmap rewrite <input>` | Rewrite content with improved structure |
22| `ml-roadmap translate <input>` | Translate ML content between languages |
23| `ml-roadmap tone <input>` | Adjust the tone of ML content (formal, casual, etc.) |
24| `ml-roadmap headline <input>` | Generate compelling headlines for ML topics |
25| `ml-roadmap outline <input>` | Create structured outlines for ML subjects |
26| `ml-roadmap stats` | Show summary statistics across all entry types |
27| `ml-roadmap export <fmt>` | Export all data (formats: `json`, `csv`, `txt`) |
28| `ml-roadmap search <term>` | Search across all entries by keyword |
29| `ml-roadmap recent` | Show the 20 most recent activity log entries |
30| `ml-roadmap status` | Health check — version, disk usage, last activity |
31| `ml-roadmap help` | Show the built-in help message |
32| `ml-roadmap version` | Print the current version (v2.0.0) |
33
34Each content command (draft, edit, optimize, etc.) works in two modes:
35- **Without arguments** — displays the 20 most recent entries of that type
36- **With arguments** — saves the input as a new timestamped entry
37
38## Data Storage
39
40All data is stored as plain-text log files in `~/.local/share/ml-roadmap/`:
41
42- Each command type gets its own log file (e.g., `draft.log`, `edit.log`, `outline.log`)
43- Entries are stored in `timestamp|value` format for easy parsing
44- A unified `history.log` tracks all activity across command types
45- Export to JSON, CSV, or TXT at any time with the `export` command
46
47Set the `ML_ROADMAP_DIR` environment variable to override the default data directory.
48
49## Requirements
50
51- Bash 4.0+ (uses `set -euo pipefail`)
52- Standard Unix utilities: `date`, `wc`, `du`, `tail`, `grep`, `sed`, `cat`
53- No external dependencies or API keys required
54
55## When to Use
56
571. **Planning your ML learning path** — use `outline` and `draft` to structure a study roadmap covering supervised learning, deep learning, NLP, computer vision, and more
582. **Creating ML educational content** — use `headline`, `hooks`, `cta`, and `hashtags` to craft engaging posts or articles about machine learning concepts
593. **Scheduling study sessions** — use `schedule` to log when you plan to study specific ML topics and track your progress over time
604. **Refining technical writing** — use `rewrite`, `tone`, and `optimize` to polish ML blog posts, documentation, or course materials
615. **Tracking content creation history** — use `stats`, `search`, and `recent` to review what you've written, find past entries, and measure productivity
62
63## Examples
64
65```bash
66# Draft a new learning plan for deep learning fundamentals
67ml-roadmap draft "Week 1: Neural network basics — perceptrons, activation functions, backprop"
68
69# Create an outline for a blog post on model selection
70ml-roadmap outline "Comparing Random Forest vs XGBoost: when to use each, key hyperparameters, pros/cons"
71
72# Generate a headline for an ML tutorial
73ml-roadmap headline "Beginner-friendly guide to building your first image classifier with PyTorch"
74
75# Schedule a study session
76ml-roadmap schedule "Saturday 10am: Work through Stanford CS229 Lecture 5 — Support Vector Machines"
77
78# Export all your entries to JSON for backup
79ml-roadmap export json
80```
81
82## Output
83
84All commands print results to stdout. Redirect to a file if needed:
85
86```bash
87ml-roadmap stats > roadmap-report.txt
88ml-roadmap export csv
89```
90
91---
92
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