Are you interested in practically mastering NLP? Then you are in the right place.
But there is a huge crowd looking to master this! To stand out among them you need to create a strong portfolio.
You can start creating your unique portfolio by starting with the below-mentioned NLP projects for beginners.
10 Beginner-Friendly NLP Project Ideas – Overview
Here’s an overview of the 10 best NLP projects for beginners:
Top 10 Natural Language Processing Projects for Beginners
Below are the top 10 simple NLP projects for beginners:
1. Text Summarization
This project involves developing a model to summarize long pieces of text into concise summaries.
You will learn about text preprocessing, feature extraction, and implementing algorithms for text summarization.
Duration: 10 hours
Project Complexity: Easy
Learning Outcome: Understanding of text preprocessing and summarization techniques.
Portfolio Worthiness: Yes
Required Pre-requisites:
- Basic Python knowledge
- Understanding of NLP concepts
- Familiarity with machine learning libraries
Resources Required:
- Code Editor
- NLP libraries (e.g., NLTK, spaCy)
Real-World Application:
- News article summarization
- Document summarization for business reports
2. Fake News Detection
This project involves creating a model to detect fake news articles using NLP techniques.
You will learn about text classification, feature extraction, and implementing machine learning algorithms.
Duration: 12 hours
Project Complexity: Easy
Learning Outcome: Understanding of text classification and feature extraction techniques.
Portfolio Worthiness: Yes
Required Pre-requisites:
- Basic Python knowledge
- Understanding of NLP concepts
- Familiarity with machine learning libraries
Resources Required:
- Code Editor
- NLP libraries (e.g., NLTK, scikit-learn)
Real-World Application:
- Social media monitoring
- News credibility verification
3. Spam SMS Classification
This project involves developing a model to classify SMS messages as spam or not spam.
You will learn about text preprocessing, feature extraction, and implementing classification algorithms.
Duration: 10 hours
Project Complexity: Easy
Learning Outcome: Understanding of text classification and preprocessing techniques.
Portfolio Worthiness: Yes
Required Pre-requisites:
- Basic Python knowledge
- Understanding of NLP concepts
- Familiarity with machine learning libraries
Resources Required:
- Code editor
- NLP libraries (e.g., NLTK, scikit-learn)
Real-World Application:
- Spam filtering for messaging apps
- Email spam detection
4. Toxic Comments Classification
This project involves creating a model to classify comments as toxic or non-toxic using NLP techniques.
You will learn about text preprocessing, feature extraction, and implementing classification algorithms.
Duration: 10 hours
Project Complexity: Easy
Learning Outcome: Understanding of text classification and sentiment analysis techniques.
Portfolio Worthiness: Yes
Required Pre-requisites:
- Basic Python knowledge
- Understanding of NLP concepts
- Familiarity with machine learning libraries
Resources Required:
- Code editor
- NLP libraries (e.g., NLTK, TensorFlow)
Real-World Application:
- Moderating online comments
- Enhancing social media user experience
5. Named Entity Recognition (NER)
This project involves developing a model to identify and classify named entities in text, such as names of people, organizations, and locations.
You will learn about tokenization, part-of-speech tagging, and NER algorithms.
Duration: 10 hours
Project Complexity: Easy
Learning Outcome: Understanding of tokenization, POS tagging, and NER.
Portfolio Worthiness: Yes
Required Pre-requisites:
- Basic Python knowledge
- Understanding of NLP concepts
- Familiarity with NER libraries
Resources Required:
- Code Editor
- NLP libraries (e.g., spaCy, NLTK)
Real-World Application:
- Information extraction from documents
- Automated customer support
6. Text Generation
This project involves creating a model that can generate text based on a given input, using techniques like RNNs or Transformers.
You will learn about sequence modeling and text generation algorithms.
Duration: 15 hours
Project Complexity: Medium
Learning Outcome: Understanding of sequence modeling and text generation.
Portfolio Worthiness: Yes
Required Pre-requisites:
- Basic Python knowledge
- Understanding of deep learning concepts
- Familiarity with machine learning libraries
Resources Required:
- Code Editor
- Deep learning libraries (e.g., TensorFlow, PyTorch)
Real-World Application:
- Creative writing tools
- Automated content creation
7. Spell and Grammar Checking
This project involves developing a model to check and correct spelling and grammar in text.
You will learn about text preprocessing, error detection, and correction techniques.
Duration: 15 hours
Project Complexity: Medium
Learning Outcome: Understanding of error detection and correction in text.
Portfolio Worthiness: Yes
Required Pre-requisites:
- Basic Python knowledge
- Understanding of NLP concepts
- Familiarity with NLP libraries
Resources Required:
- Code Editor
- NLP libraries (e.g., spaCy, NLTK)
Real-World Application:
- Text editing software
- Online writing assistants
