This repository represents my work in building intelligent systems using Natural Language Processing (NLP). The focus is on understanding how machines can process, represent, and retrieve meaning from text.
Text Data
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├── Preprocessing (cleaning, tokenization)
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├── Embeddings (Sentence Transformers)
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├── Vector Representation (numerical form of text)
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├── Similarity Computation (cosine similarity)
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├── Vector Search (FAISS)
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├── Semantic Retrieval (meaning-based search)
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├── Recommendation Logic (similar item suggestions)
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└── Classification (sentiment analysis)
The core idea behind these systems is to convert text into numerical vectors (embeddings) and use them to:
- Find similar content
- Recommend relevant items
- Understand sentiment
Instead of keyword matching, the system focuses on understanding the meaning of text.
- Text preprocessing
- Tokenization
- Embeddings (Sentence Transformers)
- Cosine similarity
- Vector search (FAISS)
- Machine learning basics
- Semantic search system
- Recommendation system
- Sentiment analysis system
All these applications are connected through a common pipeline:
Text → Embedding → Similarity → Output
- Python
- Sentence Transformers
- FAISS
- Scikit-learn
- Pandas
- NumPy
Through these projects, I gained hands-on experience in:
- Building embedding-based NLP systems
- Understanding similarity search and vector databases
- Designing real-world AI applications
- Connecting theoretical concepts with practical implementation
Janakiram