I am a recent graduate with a Bachelor's degree and a strong passion for AI, Data Science, Machine Learning, Deep Learning, and Natural Language Processing. Since my second year of university, I have been actively developing my skills in these areas through self-learning, online courses, and hands-on projects. Although I do not have formal work experience, I have built a solid foundation in data analysis, model building, and problem-solving. I am familiar with tools and technologies like Python, Pandas, Scikit-learn, TensorFlow, and NLP libraries such as NLTK and spaCy. I am currently seeking internship or entry-level opportunities where I can apply my knowledge, learn from real-world challenges, and grow as a data professional. I am enthusiastic, quick to learn, and ready to contribute to impactful projects.
0 + Projects completed
AI Engineer with a strong foundation in machine learning, natural language processing (NLP), and end to-end model deployment. Passionate about creating impactful AI solutions with explainable and ethical practices. Seeking opportunities to apply and grow in a research-driven or product-oriented AI environment.
CGPA: 3.40
GPA: 5.00
GPA: 4.95
The sample ML & Data Analytics projects
This NLP project analyzes IMDb movie reviews to classify them as positive or negative using machine learning.
This project analyzes a heart disease dataset and builds a machine learning model to predict heart disease.
This project integrates machine learning, explainable AI, and conversational AI to build a mental health support system capable of detecting psychological disorders and offering personalized counseling through a chatbot interface.
This project demonstrates a Retrieval-Augmented Generation (RAG) system that answers Bangla or English questions grounded in the HSC ২০২৬ Bangla 1st Paper textbook. Built using LangChain, Google Gemini API, and Chroma, this system enables contextual, textbook-based Q&A in a multilingual format.
This project is an advanced Retrieval-Augmented Generation (RAG) system designed for document-based question answering. It allows users to upload various documents, such as PDFs, Word files, or images, and ask questions in natural language. The system extracts text, creates vector embeddings using a Sentence Transformer, and stores them in a FAISS vector store. When a query is made, it retrieves the most relevant context and feeds it to a Large Language Model like Gemini. The LLM then generates a precise and contextually accurate answer based on the retrieved information.
The details to reach out to me!
Gopalgonj, Bangladesh
+880 157644527