Syed Ali Ammar Raza
Syed Ali Ammar Raza

Syed Ali Ammar Raza

About

Detail

Data Annotator @ CNTXT | Machine Learning Expert | Electrical Engineer | xAIESEC | Content Writer |
Islamabad, Pakistan

Contact Syed regarding: 

Flexible work
Starting at USD5/hour
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Internships
Open to unpaid internships

Timeline


work
Job
school
Education
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Project
2017
Today

Résumé


Jobs verified_user 0% verified
  • C
    Data Annotator
    CNTXT FZCo
    Apr 2024 - Current (1 year 3 months)
    • Proficient in annotating diverse data types, including images, text, audio, and video, adhering to predefined criteria and standards. • Conduct thorough quality assurance checks to ensure accuracy and consistency in annotations, employing meticulous attention to detail and error identification techniques. • Collaborate seamlessly with cross-functional teams to meet project objectives and deadlines, ensuring clear communication and swift resolution of annotation-related challenges. • Perform extensive data research and collection to support the development of large language models (LLMs). This involves identifying, gathering, and curating high-quality data from various sources to ensure the models have diverse and accurate inputs, whic
  • E
    Machine Learning Intern
    Experts Online Limited
    Jun 2022 - Nov 2022 (6 months)
    • Successfully completed a diverse range of machine learning projects, showcasing proficiency in applying algorithms like K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Random Forest Classifier and Regression, and various Classification algorithms, resulting in an average model accuracy improvement. • Demonstrated data prowess by efficiently collecting, preprocessing, and analyzing data, enhancing the quality of datasets, and achieving reduction in model training time. • Excelled in deep learning frameworks such as TensorFlow, Keras, and PyTorch, contributing to the development of state-of-the-art models that achieved boost in prediction accuracy. • Displayed exceptional problem-solving skills, proactively identifying and
  • A
    Regional Head
    AIESEC
    Apr 2020 - Mar 2021 (1 year)
    • Led as Regional Head for AIESEC in Multan, overseeing a team of 80 dedicated members, and successfully executed international exchange internship opportunities aligned with the Sustainable Development Goals (SDGs). • Provided effective mentorship and support to team members, aiding in their professional development and leadership growth, resulting in 75% improvement in team performance and project outcomes. • Acted as a prominent representative of AIESEC at various national conferences and forums, passionately advocating for the organization's mission and values, and actively contributing to a 100% increase in the organization's visibility and impact on a national level.
  • T
    Summer Intern
    Thermal Power plant Muzaffargarh
    Jun 2018 - Aug 2018 (3 months)
    • Diligently monitored the operations of boilers, turbines, and other power generating equipment, ensuring consistent and reliable plant performance. • Actively participated in comprehensive training sessions focused on plant operations and maintenance practices, enhancing knowledge and skills.
Education verified_user 0% verified
  • IBM
    Python for Data Science and AI
    IBM
    May 2025
  • University of Michigan
    Getting Started with Python (Specialization)
    University of Michigan
    Nov 2024 - Dec 2024 (2 months)
  • Kaggle
    Pandas
    Kaggle
    Jun 2023 - Aug 2023 (3 months)
  • Kaggle
    Python
    Kaggle
    May 2023
  • N
    MSc.
    National University of Science & Technology (NUST)
    Jan 2021 - Current (4 years 6 months)
  • I
    BS Electrical
    Institute of Engineering & Technology (NFC)
    Jan 2017 - Jan 2021 (4 years 1 month)
Projects verified_user 0% verified
  • S
    Stroke Detection using YOLOv4-Tiny
    Feb 2025 - Mar 2025 (2 months)
    • Developed a real-time Stroke Detection system using YOLOv4-Tiny and computer vision • Trained the model with curated datasets, achieving high accuracy and mAP.
  • A
    Automated Call Transcription Integration Project
    Jun 2024 - Jul 2024 (2 months)
    • Seamlessly integrated OpenAI's WhisperAI with Google Sheets to create an automated call transcription solution for a busy call center. • Addressed the challenge of manually transcribing a high volume of daily call recordings with an AI-powered solution
  • C
    Credit Card Loan Eligibility Prediction using Logistic Regression and Exploratory Data Analysis
    May 2024
    • Exploratory Data Analysis (EDA): Conducted EDA to understand the dataset and its variables. • Model Implementation: Used logistic regression to predict credit card loan eligibility. Processing: Encoded 'Loan_Status' target variable, split data into features (X) and target variable (y), and divided it into training/testing sets. Model Training and Evaluation: Trained the logistic regression model, made predictions on test data, and evaluated performance using accuracy metrics and classification report.
  • P
    Performance Evaluation
    Apr 2024
  • R
    Real-Time Web Data Retrieval with LLM-RAG Chatbot
    Mar 2024 - Apr 2024 (2 months)
    • Developed an innovative real-time web data retrieval chatbot using Langchain, OpenAI's GPT-4, and Streamlit. • Utilized advanced LLM-RAG architecture to enhance the chatbot's knowledge base for precise and relevant answers. • Designed a user-friendly interface with Streamlit, making the solution accessible to users with varying technical expertise.
  • D
    Deep Learning Time Series Forecasting with LSTM
    Nov 2023 - Dec 2023 (2 months)
    • Developed LSTM Model: Implemented a Long Short-Term Memory (LSTM) neural network for time series forecasting using PyTorch. Processed dataset from an Excel file and normalized it using Min-Max scaling. • Model Training and Evaluation: Trained the LSTM model to predict multiple features simultaneously and evaluated performance using Mean Squared Error. • Baseline Comparison: Compared LSTM model performance with a baseline model using the 'Thinking Fast and Slow' algorithm.
  • Y
    Yeast Classification Using Machine Learning Algorithms
    Oct 2023 - Nov 2023 (2 months)
    • Implemented and Compared Algorithms: Utilized SVM, Random Forest, and Logistic Regression for yeast classification using Python's scikit-learn. Conducted data preprocessing, split dataset into training/testing sets, and fine-tuned hyper parameters. • Evaluated models using ROC curves, accuracies, and confusion matrices, providing a detailed comparison of each algorithm.