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Mohamed Sameh

Mohamed Sameh

Machine Learning Intern

Egypt flagAssiut, Egypt
$13.00/hrIntermediateAppenData Annotation TechScale AI

Key Skills

Software

AppenAppen
Data Annotation TechData Annotation Tech
Scale AIScale AI

Top Subject Matter

Machine Learning
Embedded Engineering
Software Engineering

Top Data Types

TextText
ImageImage
Computer Code ProgrammingComputer Code Programming

Top Task Types

ClassificationClassification
RLHFRLHF
Function CallingFunction Calling

Freelancer Overview

Machine Learning Intern. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Education includes Bachelor of Science, Faculty of Engineering (2024). AI-training focus includes labeling workflows including Classification.

IntermediateEnglish

Labeling Experience

Machine Learning Intern

Classification
As a Machine Learning Intern, I developed skills in classical machine learning methodologies and applied them to real-world problems. I implemented various algorithms and models using Python and scikit-learn, focusing on building effective solutions. This role enhanced my expertise in managing and processing highly imbalanced datasets through advanced resampling methods. • Applied regression, classification, clustering, and dimensionality reduction techniques in practical scenarios. • Tackled multiple machine learning competitions on Kaggle to improve preprocessing and model selection skills. • Utilized SMOTE and cost-sensitive learning strategies to address class imbalances and boost accuracy. • Collaborated in a team environment to deliver machine learning solutions using Python and scikit-learn.

As a Machine Learning Intern, I developed skills in classical machine learning methodologies and applied them to real-world problems. I implemented various algorithms and models using Python and scikit-learn, focusing on building effective solutions. This role enhanced my expertise in managing and processing highly imbalanced datasets through advanced resampling methods. • Applied regression, classification, clustering, and dimensionality reduction techniques in practical scenarios. • Tackled multiple machine learning competitions on Kaggle to improve preprocessing and model selection skills. • Utilized SMOTE and cost-sensitive learning strategies to address class imbalances and boost accuracy. • Collaborated in a team environment to deliver machine learning solutions using Python and scikit-learn.

2022 - 2022

Education

F

Faculty of Engineering

Bachelor of Science, Computer and Control Systems

Bachelor of Science
2020 - 2024

Work History

E

EME Innovation Hub

Machine Learning Intern

Assiut
2022 - 2022