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Aaditya Singh

@aadityasingh

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Machine Learning Engineer focused on deep learning, NLP, and predictive analytics through real-world deployments.

India

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What I'm looking for

I’m looking for a role where I can apply machine learning, deep learning, and NLP to build predictive models and deploy real solutions—working with strong data practices, meaningful evaluation, and a team that values iteration and impact.

Final-year B.Tech Computer Science student with hands-on experience in Machine Learning, Data Science, NLP, and Computer Vision through three internships and 10+ projects.

As a Data Science Intern at CertED Technologies, developed a music recommendation system by clustering 160K+ songs and deploying it through an interactive Streamlit application. During my AICTE SkillsBuild internship, built an EV adoption forecasting model and dashboard to analyze future EV growth trends. In the Google for Developers AI/ML internship, developed a CNN-based Face Mask Detection system that achieved over 93% test accuracy.

Additionally, developed a MuRIL-based multilingual hate speech detection system for Hindi, English, and Hinglish. One of the key challenges was handling Hinglish text, where users frequently mix Hindi and English within the same sentence, use non-standard spellings, and write Hindi words in Roman script. Addressed these challenges through data preprocessing, label validation, and model fine-tuning to improve classification performance across languages. Experienced in end-to-end machine learning development, from data preparation and model training to evaluation and deployment.

Experience

Work history, roles, and key accomplishments

AI/ML Virtual Intern

Google for Developers – AICTE EduSkills

Jan 2026 - Mar 2026 (2 months)

Built a CNN-based face mask detection system achieving 93%+ test accuracy for masked vs. unmasked classification. Developed a Django web app for real-time image upload and mask prediction using the trained model.

TensorFlow Python Computer Vision Django Machine Learning Deep Learning

Artificial Intelligence Intern

AICTE SkillsBuild

Jul 2025 - Jul 2025 (0 months)

Forecasted future EV adoption to support infrastructure planning using Random Forest, achieving R2 = 1.00. Predicted county-level EV growth from time-series registration data and validated model performance using evaluation metrics.

Python scikit learn Random Forest Time Series Analysis Model Evaluation Machine Learning Streamlit Data Analysis

Data Science Intern

CertEd Technologies

Jun 2025 - Jun 2025 (0 months)

Developed a music suggestion system using K-Means clustering, organizing over 160K tracks into 10 clusters based on audio feature similarity.
Executed data preprocessing and exploratory data analysis , including duplicate removal, feature scaling and transformation of over 15 audio
features.
Launched a Streamlit app integrating trained ML pipelines, achieving a Davies-Bouldin Index of 0.49.

Python Streamlit Machine Learning Data Science scikit learn Pandas NumPy