Natural Language Processing Internship
Teach computers to understand language: text cleaning, TF-IDF and classic classifiers, embeddings, transformer fine-tuning, NER, summarisation, semantic search, Indian languages and speech to text.
What you'll work on
- Level 1: Foundations - What NLP is, Text cleaning, Tokenization and more
- Level 2: Building real features - Fine-tune a text classifier, Named entity recognition model, Text summarisation and more
- Level 3: Professional work and capstone - Speech to text, Bias check, Error analysis and more
Full syllabus: all 30 graded tasks
These are the actual tasks the internship portal assigns for this domain. You submit each one with proof (a GitHub link or document) and a mentor reviews it.
Level 1: Foundations (12 tasks)
- 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.
- Word frequencies and n-grams. Find the top words, bigrams and trigrams in a set of product reviews and plot them as a bar chart and a word cloud.
- Regular expressions for text. Extract emails, Indian phone numbers, PIN codes and dates from 50 lines of sample text using regular expressions.
- Bag of words and TF-IDF. Turn 200 documents into bag-of-words and TF-IDF vectors with scikit-learn and show the top TF-IDF words per document.
- Spam classifier. Train a Naive Bayes spam classifier on the free SMS Spam dataset and report accuracy, precision, recall and the confusion matrix.
- Part-of-speech and named entities. Run spaCy on 10 news paragraphs and list the people, organisations, places and dates it finds. Mark its mistakes.
- Word embeddings. Load pretrained word vectors (GloVe or fastText), find similar words and solve 5 analogies like king - man + woman.
- Sentiment with a pretrained model. Use a Hugging Face sentiment pipeline on 50 reviews and compare its labels with your own.
- Language detection. Detect the language of 30 sentences mixing English, Tamil, Hindi and Tanglish, and note where detection fails.
Level 2: Building real features (10 tasks)
- Fine-tune a text classifier. Fine-tune DistilBERT or a small multilingual model on a labelled dataset (news topics or reviews) and compare it with your TF-IDF model.
- Named entity recognition model. Label 200 sentences with your own entity types (for example COURSE and COLLEGE) and train a spaCy NER model.
- Text summarisation. Summarise 10 long articles with an extractive method and a transformer model, and rate both summaries.
- Semantic search. Build search over 500 FAQs using sentence embeddings so users find answers even with different words.
- Topic modelling. Find topics in 1,000 customer complaints with LDA or BERTopic and name each topic.
- Indian languages. Classify or translate text in one Indian language using IndicNLP resources or a multilingual model, and report the results.
- Question answering. Build an extractive question-answering demo over your college prospectus using a pretrained QA model.
- Evaluation metrics. Explain and compute accuracy, F1, BLEU and ROUGE on your models' outputs, and say which metric fits which task.
- Text API. Serve your best classifier as a FastAPI endpoint with input length limits and clear error messages.
- Chatbot intents. Build an intent classifier for a college help bot with 10 intents and fallback replies, and test it with 50 messages.
Level 3: Professional work and capstone (8 tasks)
- Speech to text. Transcribe 10 audio clips with Whisper (including Indian-English accents), measure the word error rate and clean the transcripts.
- Bias check. Test your sentiment model for bias with sentences that differ only by name, gender or region, and report what you find.
- Error analysis. Find 30 mistakes your classifier makes, group them by cause (sarcasm, negation, mixed language) and improve one group.
- Model optimisation. Make your transformer model faster with distillation, quantization or ONNX export and compare speed and accuracy.
- Web app. Build a Streamlit or Gradio app for your NLP model and host it free on Hugging Face Spaces.
- Data and privacy. Document how your dataset was collected, its licence and how personal data (names, phones) is removed before training.
- Model card. Write a model card: data, training, metrics, limits, languages supported and intended use.
- Capstone: NLP product. Ship a complete NLP app (review analyser, complaint router, multilingual FAQ bot or resume screener) with a live link, repo and a 2-minute demo video.
Real software work, not just training
Every Natural Language Processing task is reviewed by our team and saved to your portfolio. Interns who pass the readiness check can take real work from Training Trains, W3AppDevelopers and DomainHostly projects on the Company Work board.
Frequently asked questions
Do I need experience to join the Natural Language Processing internship?+
Basic Python is needed. Free Colab or Kaggle GPUs are enough for fine-tuning.
What will I do in the Natural Language Processing internship?+
You complete 30 graded tasks in three levels: foundations, real features, and professional work. Each task is submitted with GitHub or document proof and reviewed. The final capstone is: Capstone: NLP product.
Do I get a certificate for the Natural Language Processing internship?+
Yes. After your required tasks are approved, your attendance is above 75% and you pass the proctored final exam, you get a graded completion certificate that anyone can verify at trainingtrains.com/verify.
Can I do the Natural Language Processing internship online?+
Yes. You can do it fully online from home, or offline at our Erode and Perundurai branches. Tasks, quizzes and mentor reviews are the same in both modes.
Available both online (work from home) and offline at our Erode and Perundurai branches - see the full internship program details for departments, duration, and the application process.