https://github.com/denisecase/nlp-01-getting-started
Professional Python project for Web Mining and Applied NLP.
https://github.com/denisecase/nlp-01-getting-started
data-analytics github-actions natural-language-processing nlp professional-python python reproducible-workflows src-layout
Last synced: about 22 hours ago
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Professional Python project for Web Mining and Applied NLP.
- Host: GitHub
- URL: https://github.com/denisecase/nlp-01-getting-started
- Owner: denisecase
- License: mit
- Created: 2026-03-08T22:55:40.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-03-09T05:16:03.000Z (3 months ago)
- Last Synced: 2026-03-09T09:58:10.349Z (3 months ago)
- Topics: data-analytics, github-actions, natural-language-processing, nlp, professional-python, python, reproducible-workflows, src-layout
- Language: Jupyter Notebook
- Homepage: https://denisecase.github.io/nlp-01-getting-started/
- Size: 452 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
- Agents: .github/AGENTS.md
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README
# nlp-01-getting-started
[](#)
[](./LICENSE)
> Professional Python project for Web Mining and Applied NLP.
Web Mining and Applied NLP focus on retrieving, processing, and analyzing text from the web and other digital sources.
This course builds those capabilities through working projects.
In the age of generative AI, durable skills are grounded in real work:
setting up a professional environment,
reading and running code,
understanding the logic,
and pushing work to a shared repository.
Each project follows a similar structure based on professional Python projects.
These projects are **hands-on textbooks** for learning Web Mining and Applied NLP.
## This Project
This is the **getting started** project.
The goal is to copy this repository, set up your environment, run the example script and notebook, and push your work to GitHub.
Then, you'll change the authorship to make the project yours and explore the structure.
No major code changes are required.
You'll work with just these areas:
- **notebooks/** - Jupyter notebooks for exploration
- **src/nlp/** - Python code (verifies .venv/)
- **pyproject.toml** - update authorship, links, and dependencies
- **zensical.toml** - update authorship and links
The goal is just to confirm you can run projects on your machine.
Once you get the first project running successfully,
the rest of the course is much easier.
## First: Follow These Instructions
Follow the [step-by-step workflow guide](https://denisecase.github.io/pro-analytics-02/workflow-b-apply-example-project/) to complete:
1. Phase 1. **Start & Run**
2. Phase 2. **Change Authorship**
3. Phase 3. **Read & Understand**
## Challenges
Challenges are expected.
Sometimes instructions may not quite match your operating system.
When issues occur, share screenshots, error messages, and details about what you tried.
Working through issues is an important part of implementing professional projects.
## Success
After completing Phase 1. **Start & Run**, you'll have your own GitHub project,
running on your machine, and running the example will print out:
```shell
========================
Pipeline executed successfully!
========================
```
And a new file named `project.log` will appear in the project folder.
Once you see it, you're 90% of the way there.
After that, you'll just make the project yours and get started exploring.
## Command Reference
The commands below are used in the workflow guide above.
They are provided here for convenience.
Follow the guide for the **full instructions**.
Show command reference
### In a machine terminal (open in your `Repos` folder)
After you get a copy of this repo in your own GitHub account,
open a machine terminal in your `Repos` folder:
```shell
# Replace username with YOUR GitHub username.
git clone https://github.com/username/nlp-01-getting-started
cd nlp-01-getting-started
code .
```
### In a VS Code terminal
```shell
uv self update
uv python pin 3.14
uv sync --extra dev --extra docs --upgrade
uvx pre-commit install
git add -A
uvx pre-commit run --all-files
# Later, we install spacy data model and
# en_core_web_sm = english, core, web, small
# It's big: spacy+data ~200+ MB w/ model installed
# ~350–450 MB for .venv is normal for NLP
# uv run python -m spacy download en_core_web_sm
# First, run the module
# IMPORTANT: Close each figure after viewing so execution continues
uv run python -m nlp.web_words_case
# Then, open the notebook.
# IMPORTANT: Select the kernel and Run All:
# notebooks/web_words_case.ipynb
uv run ruff format .
uv run ruff check . --fix
uv run zensical build
git add -A
git commit -m "update"
git push -u origin main
```
## Notes
- Use the **UP ARROW** and **DOWN ARROW** in the terminal to scroll through past commands.
- Use `CTRL+f` to find (and replace) text within a file.
## Example Artifact (Output)
