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Machine Learning Engineer | NLP Explorer | Data Science Enthusiast | Author
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FineTune-Phi-2-LLM-using-PEFT-QLora
February 28, 2024 – October 4, 2024
Fine-tuning Large Language Models (LLMs) is a crucial step in adapting these powerful models to specific tasks or domains. In this seminar code tutorial, we will explore how to perform fine-tuning using QLoRA (Quantized LoRA), a memory-efficient iteration of LoRA (Low-Rank Adaptation), for parameter-efficient fine-tuning.
View ProjectAirBnb-NYC-Listing
February 6, 2024 – October 4, 2024
New York City Airbnb Open Data | Peforming Data Wrangling, Analysis, Visualization, Regression, Classification, Hypothesis-Testing
View ProjectCOVID19-World-Analysis
March 30, 2020 – March 30, 2020
COVID19-World-Analysis — GitHub repository
View ProjectAdverAttack-on-Text
March 24, 2020 – March 24, 2020
Adversarial Attack on Text data using Nlpaug library
View ProjectSentence_Similarity
December 18, 2019 – December 18, 2019
Uniqueness & Analysis of Sentence(Log Loss is used for performance evaluation)
View ProjectTopicModeling_gensim
December 13, 2019 – December 20, 2019
Topic modeling of a headline dataset using Gensim. LDA is used to evaluate performance.
View ProjectSpeech-to-Text
December 10, 2019 – December 10, 2019
Flask application that takes speech as input and returns text as output
View ProjectTitanic-ML-Disaster-Prediction
December 10, 2019 – October 4, 2024
Titanic Data Science Solutions This notebook is the solution to the Titanic: MACHINE LEARNING for Disaster Workflow stages The competition solution workflow goes through seven stages described in the Data Science Solutions book. Question or problem definition. Acquire training and testing data. Wrangle, prepare, cleanse the data. Analyze, identify patterns, and explore the data. Model, predict and solve the problem. Visualize, report, and present the problem solving steps and final solution. Submiting the results. Problem Statement Competition sites like Kaggle define the problem to solve or questions to ask while providing the datasets for training your data science model and testing the model results against a test dataset. The question or problem definition for Titanic Survival competition is described here at Kaggle. Workflow goals The data science solutions workflow solves for seven major goals. Classifying. We may want to classify or categorize our samples. We may also want to un
View ProjectCultural Fit Analysis
The candidate's projects are exclusively personal and lack diversity in team collaboration or real-world business context, which might indicate a limited understanding of collaborative development environments and industry practices. The focus on individual projects, while demonstrating initiative, does not provide insight into their ability to integrate into a team-oriented culture or adapt to organizational workflows. The experience level is 0, suggesting a lack of professional experience.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.