Verified Expert in Engineering
NLP and Machine Learning Developer
Nilan is a natural language processing and machine learning expert with a bachelor's degree in computer science and a master's degree in data science specializing in computational linguistics. Nilan has vast experience building large-scale recommendation systems, personalization technology, and NLP algorithms. A Kaggle expert, he has well-cited publications in this domain.
MacOS, Slack, Zoom, Visual Studio Code (VS Code), Jupyter Notebook
The most amazing...
...thing I've ever built is Convex, an NLP library for part-of-speech (POS) tagging using character and word-level embedding neural nets.
Machine Learning Engineer
- Built and deployed multiple personalization ML pipelines to power the main user feed for the app. The pipelines were built using Python, Kafka, AWS S3, scikit-learn, and NLTK and deployed to AWS.
- Designed, architected, and built an end-to-end search solution using Python and Elasticsearch. The analytics pipeline that drives data-driven decisions and better search results' relevance ranking was built using Kafka, Amazon Pinpoint, and AWS S3.
- Designed, architected, and built an end-to-end autocomplete solution using Python and Elasticsearch. The analytics pipeline that drives data-driven decisions and relevance ranking of search results was built using Kafka, Amazon Pinpoint, and AWS S3.
- Collaborated with the team to test out all code samples and make sure it was easy for users to replicate the projects from the book. The code consisted of neural networks built in PyTorch and various other pre-processing utilities in NLTK.
- Worked with the editing team to review all the book chapters and make necessary corrections, technical and otherwise.
- Suggested various improvements in terms of the book content.
Machine Learning Engineer
- Developed a deep neural net with multiple heads using ELMo embeddings to identify phrases that could be used to generate quizzes and achieve other downstream tasks. The model was built using Python, PyTorch, and Flair and deployed to AWS.
- Developed a pipeline using spaCy and Python to extract triplets from textual data, build relations using them, and represent it in the form of a knowledge graph.
- Led initiatives to build a dataset to train models based on implicit user feedback.
- Developed a heuristic algorithm using named-entity recognition (NER), spaCy, and the natural language toolkit (NLTK) to identify cooking ingredients from recipe instruction data.
- Created an algorithm to use identified ingredients to generate optimal cooking instructions to reduce friction for end-users.
- Bundled the model into an API and worked with the in-house tech team to integrate it into the entire stack.
Multi-label Classifier for Toxic Comments
Categorical Embedding Encoderhttps://github.com/nilansaha/CategoricalEmbeddingEncoder
Python, Go, GraphQL
SpaCy, PyTorch, Scikit-learn, Natural Language Toolkit (NLTK), Keras
Natural Language Processing (NLP), Machine Learning, GPT, Generative Pre-trained Transformers (GPT), Software, Software Development, Computer Science, Deep Learning, Data Structures, Technical Writing, Computational Linguistics
Software Design Patterns, Apache Kafka, MacOS, Visual Studio Code (VS Code), Jupyter Notebook, Amazon Web Services (AWS)
Databases, Elasticsearch, Amazon DynamoDB, Neo4j
Master's Degree in Data Science and Computational Linguistics
University of British Columbia - Vancouver, Canada
Bachelor's Degree in Computer Science
Institute of Engineering and Management - Kolkata, India
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