Description
Handwriting Pattern Recognizer: A tool for Urdu language learners, helping teachers monitor and assess handwriting patterns in early childhood education, offering feedback to parents.
Next Word Predictor: A language model using word embeddings and RNNs to predict the next word in a sequence, similar to predictive text systems.
Topic Modeling: Utilizes deep learning (BERTopic) to classify and visualize topics in news articles, extracting insights from large text corpora.
Sales Chat Bot: An NLP-driven chatbot that automates customer interactions, providing product recommendations and streamlining the sales process.
Practical Use Case and User Story
Handwriting Recognizer: As a teacher, I want to automatically assess students’ Urdu handwriting patterns, ensuring correct stroke sequences, so I can provide accurate feedback on writing accuracy and improvement areas.
Next Word Predictor: As a user, I want a predictive text system that suggests the next word based on my input, making typing faster and more efficient in chat and writing applications.
Topic Modeling: As a news aggregator, I want to classify and visualize key topics from large datasets of news articles, helping me identify trends and organize content effectively.
Sales Chat Bot: As a customer, I want a chatbot to answer product questions, recommend items, and assist with order management, improving my shopping experience through quick, automated support.
Tech Stack Involved
Programming Languages:
- Python: For ML models, NLP processing, data manipulation, scripting.
- JavaScript/Node.js: For chat interfaces, APIs, backend services.
Natural Language Processing (NLP):
- NLP Libraries:
- spaCy: Text processing and entity recognition.
- NLTK: Linguistic data processing and basic NLP tasks.
- Hugging Face Transformers: State-of-the-art models like BERT and GPT.
- Dialogflow/Rasa: Conversational agents and chat bots.
Machine Learning Frameworks:
- TensorFlow: Building and deploying ML models.
- PyTorch: Custom models and advanced ML tasks.
- scikit-learn: Classical ML algorithms and model evaluation.
Data Storage and Management:
- Databases: SQL Databases (e.g., PostgreSQL, MySQL): Structured data storage and transactional operations.
- NoSQL Databases (e.g., MongoDB, DynamoDB): Unstructured or semi-structured data and flexible schemas.