InternshipTools and first-week setup
The tools a Natural Language Processing intern uses and how to set up your workspace in week one.
These are the main tools for Natural Language Processing tasks. You do not need all of them on day one; the Level 1 tasks tell you when to install each one.
- Python
- NLTK & spaCy
- scikit-learn
- Hugging Face Transformers
- sentence-transformers
- Whisper
- Gradio / Streamlit
Set up your workspace
- Create a free GitHub account if you do not have one, with a professional username, and set up your LinkedIn profile: every task is submitted with a LinkedIn post.
- Create one repository for the internship, for example natural-language-processing-internship, with a README that says who you are and what the repo holds.
- Keep one folder per task, named with the task number and title, so mentors find your work quickly.
- Commit small changes every day with clear messages. Your daily work report uses these commits as proof.
- Never commit passwords, API keys or personal data. Put secrets in a .env file and add it to .gitignore.
Your first tasks
| Task | What to do |
|---|---|
| What NLP is | In a repo named nlp-internship, explain NLP and 6 real uses (search, chatbots, spam filters, translation, sentiment, summarising) with an Indian example for each. |
| Text cleaning | Clean 100 messy social-media comments in Python: lowercase, remove URLs, emojis and punctuation, and show before and after. |
| Tokenization | Tokenize English and Tamil or Hindi sentences with NLTK or spaCy and a subword tokenizer, and compare the tokens. |
| Stop words, stemming and lemmatization | Apply stop-word removal, stemming and lemmatization to 20 sentences and explain when each helps or hurts. |