AI Intern
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07.2025 - 08.2025
- Developed and implemented a machine learning-based recommendation engine to personalize course suggestions for users based on their learning patterns and preferences.
- Collected and preprocessed large datasets using Pandas and NumPy, and applied clustering and collaborative filtering techniques to segment users and generate relevant recommendations.
- Built, trained, and optimized ML models using Scikit-learn and TensorFlow, improving recommendation accuracy by 20% over baseline.
- Collaborated with the product and data engineering teams to integrate the model into the platform, enabling real-time suggestions.
- Utilized tools like Jupyter Notebook, Git, and Google Colab for development and experimentation, and participated in regular sprint reviews and code reviews.