Ezmamul H.

Key Skills

Software

Don't disclose

Top Subject Matter

AI/ML for recommender systems in an e-learning platform

Generative AI/ML model training concepts (coursework/certifications)

Top Data Types

Computer Code Programming

Top Task Types

Text Generation

Fine-tuning

Freelancer Overview

Project: E-Learning Platform with Recommendation System. Core strengths include scikit-learn and Don't disclose. Education includes Master of Engineering, United Group of Institutions and Bachelor of Technology, Kamla Nehru Institute of Technlogy. AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Text Generation and Fine-tuning.

Labeling Experience

AI/ML coursework and certifications (edX/AWS, Azure, Google Cloud, DeepLearning.AI, Microsoft)

Don't discloseFine-tuning

Completed multiple AI/LLM-related coursework and certifications that likely involve training and hands-on work with AI models. The resume references “Generative AI with Large Language Models,” “Azure AI Fundamentals,” and other AI/ML programs, indicating exposure to model training workflows and evaluation. However, no specific data labeling, annotation, or LLM training-from-data role responsibilities are explicitly described. • Studied Generative AI with large language models (edX, AWS) • Completed AI and ML fundamentals courses (e.g., Azure AI Fundamentals, Google Cloud) • Earned AI for Everyone (Andrew Ng) certification indicating applied ML concepts • Completed Microsoft Azure AI Engineer Associate certification for broader AI engineering knowledge

Not specified

Project: E-Learning Platform with Recommendation System

Text Generation

Developed an AI-enabled e-learning recommendation system using machine learning techniques (Collaborative Filtering) as part of a course project. The work involved preparing and using user interaction signals to train and improve the recommendation logic rather than manual data labeling. Scikit-learn and related tooling were used to implement and evaluate the recommendation component within the platform. • Used collaborative filtering concepts to generate personalized course recommendations • Implemented backend logic to support high-concurrency user access (500+ users) • Worked with an integrated tech stack for model/feature computation and serving results • Collaborated with the application design to connect recommendation outputs to course browsing UI

Not specified

Education