Machine Learning Roadmap
Machine Learning Roadmap v2.0.0 — a content toolkit for drafting, editing, optimizing, and managing machine learning content. Create outlines, write headlines, generate CTAs, manage hashtags, rewrite content, translate text, and adjust tone — all tracked with timestamped entries stored locally.
Commands
Run scripts/script.sh <command> [args] to use.
| Command |
Description |
draft <input> |
Record a draft entry. Without args, shows the 20 most recent draft entries. |
edit <input> |
Record an edit entry. Without args, shows recent edit entries. |
optimize <input> |
Record an optimization entry. Without args, shows recent optimize entries. |
schedule <input> |
Record a scheduling entry. Without args, shows recent schedule entries. |
hashtags <input> |
Record a hashtags entry. Without args, shows recent hashtags entries. |
hooks <input> |
Record a hooks entry. Without args, shows recent hooks entries. |
cta <input> |
Record a call-to-action entry. Without args, shows recent CTA entries. |
rewrite <input> |
Record a rewrite entry. Without args, shows recent rewrite entries. |
translate <input> |
Record a translation entry. Without args, shows recent translate entries. |
tone <input> |
Record a tone adjustment entry. Without args, shows recent tone entries. |
headline <input> |
Record a headline entry. Without args, shows recent headline entries. |
outline <input> |
Record an outline entry. Without args, shows recent outline entries. |
stats |
Show summary statistics across all entry types (counts, data size). |
export <fmt> |
Export all data in json, csv, or txt format. |
search <term> |
Search all log files for a term (case-insensitive). |
recent |
Show the 20 most recent entries from the activity history. |
status |
Health check — version, data directory, entry count, disk usage. |
help |
Show help message with all available commands. |
version |
Show version string (machine-learning-roadmap v2.0.0). |
Data Storage
All data is stored in ~/.local/share/machine-learning-roadmap/:
- Each command type writes to its own
.log file (e.g., draft.log, headline.log, translate.log)
- Entries are timestamped in
YYYY-MM-DD HH:MM|<value> format
- A unified
history.log tracks all actions across command types
- Export files are written to the same directory as
export.json, export.csv, or export.txt
Requirements
- Bash 4+ with
set -euo pipefail
- Standard Unix utilities (
date, wc, du, tail, grep, sed, cat)
- No external dependencies — works out of the box on Linux and macOS
When to Use
- Drafting ML content — use
draft and outline to capture ideas and structure articles, blog posts, or course materials about machine learning topics
- Headline and hook creation — record
headline and hooks entries to brainstorm attention-grabbing titles and opening lines for ML content
- Content optimization — use
optimize, rewrite, and tone to track iterations as you refine ML tutorials, documentation, or marketing copy
- Multi-language content — record
translate entries when adapting ML learning materials for different language audiences
- Content scheduling and CTAs — use
schedule and cta to plan publication timelines and track call-to-action variations for ML courses or newsletters
Examples
# Draft a new ML blog post idea
machine-learning-roadmap draft "Introduction to Neural Networks: A Beginner's Guide"
# Create an outline for a tutorial
machine-learning-roadmap outline "1. What is ML? 2. Supervised vs Unsupervised 3. Tools 4. Practice Projects"
# Record a headline variation
machine-learning-roadmap headline "5 Python Libraries Every ML Engineer Must Know in 2025"
# Generate hashtags for social media
machine-learning-roadmap hashtags "#MachineLearning #AI #DeepLearning #Python #DataScience"
# Export all content data as CSV
machine-learning-roadmap export csv
# Search for entries mentioning a topic
machine-learning-roadmap search "neural"
# View summary statistics
machine-learning-roadmap stats
Output
All commands print results to stdout. Each recording command confirms the save and shows the total entry count for that category. Redirect output to a file with:
machine-learning-roadmap stats > report.txt
Configuration
Set the DATA_DIR inside the script or modify the default path ~/.local/share/machine-learning-roadmap/ to change where data is stored.
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1---2name: machine-learning-roadmap-23description: Follow a structured ML roadmap connecting concepts, tools, and learning resources. Use when planning study paths, discovering resources, mapping skills.4license: MIT5---6
7# Machine Learning Roadmap
8
9Machine Learning Roadmap v2.0.0 — a content toolkit for drafting, editing, optimizing, and managing machine learning content. Create outlines, write headlines, generate CTAs, manage hashtags, rewrite content, translate text, and adjust tone — all tracked with timestamped entries stored locally.
10
11## Commands
12
13Run `scripts/script.sh <command> [args]` to use.
14
15| Command | Description |
16|---------|-------------|
17| `draft <input>` | Record a draft entry. Without args, shows the 20 most recent draft entries. |
18| `edit <input>` | Record an edit entry. Without args, shows recent edit entries. |
19| `optimize <input>` | Record an optimization entry. Without args, shows recent optimize entries. |
20| `schedule <input>` | Record a scheduling entry. Without args, shows recent schedule entries. |
21| `hashtags <input>` | Record a hashtags entry. Without args, shows recent hashtags entries. |
22| `hooks <input>` | Record a hooks entry. Without args, shows recent hooks entries. |
23| `cta <input>` | Record a call-to-action entry. Without args, shows recent CTA entries. |
24| `rewrite <input>` | Record a rewrite entry. Without args, shows recent rewrite entries. |
25| `translate <input>` | Record a translation entry. Without args, shows recent translate entries. |
26| `tone <input>` | Record a tone adjustment entry. Without args, shows recent tone entries. |
27| `headline <input>` | Record a headline entry. Without args, shows recent headline entries. |
28| `outline <input>` | Record an outline entry. Without args, shows recent outline entries. |
29| `stats` | Show summary statistics across all entry types (counts, data size). |
30| `export <fmt>` | Export all data in `json`, `csv`, or `txt` format. |
31| `search <term>` | Search all log files for a term (case-insensitive). |
32| `recent` | Show the 20 most recent entries from the activity history. |
33| `status` | Health check — version, data directory, entry count, disk usage. |
34| `help` | Show help message with all available commands. |
35| `version` | Show version string (`machine-learning-roadmap v2.0.0`). |
36
37## Data Storage
38
39All data is stored in `~/.local/share/machine-learning-roadmap/`:
40
41- Each command type writes to its own `.log` file (e.g., `draft.log`, `headline.log`, `translate.log`)
42- Entries are timestamped in `YYYY-MM-DD HH:MM|<value>` format
43- A unified `history.log` tracks all actions across command types
44- Export files are written to the same directory as `export.json`, `export.csv`, or `export.txt`
45
46## Requirements
47
48- Bash 4+ with `set -euo pipefail`
49- Standard Unix utilities (`date`, `wc`, `du`, `tail`, `grep`, `sed`, `cat`)
50- No external dependencies — works out of the box on Linux and macOS
51
52## When to Use
53
541. **Drafting ML content** — use `draft` and `outline` to capture ideas and structure articles, blog posts, or course materials about machine learning topics
552. **Headline and hook creation** — record `headline` and `hooks` entries to brainstorm attention-grabbing titles and opening lines for ML content
563. **Content optimization** — use `optimize`, `rewrite`, and `tone` to track iterations as you refine ML tutorials, documentation, or marketing copy
574. **Multi-language content** — record `translate` entries when adapting ML learning materials for different language audiences
585. **Content scheduling and CTAs** — use `schedule` and `cta` to plan publication timelines and track call-to-action variations for ML courses or newsletters
59
60## Examples
61
62```bash
63# Draft a new ML blog post idea
64machine-learning-roadmap draft "Introduction to Neural Networks: A Beginner's Guide"
65
66# Create an outline for a tutorial
67machine-learning-roadmap outline "1. What is ML? 2. Supervised vs Unsupervised 3. Tools 4. Practice Projects"
68
69# Record a headline variation
70machine-learning-roadmap headline "5 Python Libraries Every ML Engineer Must Know in 2025"
71
72# Generate hashtags for social media
73machine-learning-roadmap hashtags "#MachineLearning #AI #DeepLearning #Python #DataScience"
74
75# Export all content data as CSV
76machine-learning-roadmap export csv
77
78# Search for entries mentioning a topic
79machine-learning-roadmap search "neural"
80
81# View summary statistics
82machine-learning-roadmap stats
83```
84
85## Output
86
87All commands print results to stdout. Each recording command confirms the save and shows the total entry count for that category. Redirect output to a file with:
88
89```bash
90machine-learning-roadmap stats > report.txt
91```
92
93## Configuration
94
95Set the `DATA_DIR` inside the script or modify the default path `~/.local/share/machine-learning-roadmap/` to change where data is stored.
96
97---
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