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Applied AI & ML
- AI solution design (NLP, Computer Vision, Recommendation Systems)
- Model selection by use case; ML pipelines (data → features → model → evaluation)
- Feature engineering, evaluation (precision, recall, F1); interpreting outputs for product
- Responsible AI (bias, explainability, trust)
- Hands-on: NLP (Sentiment, Fake News), CV (Fake Image, OpenCV), Recommendations, Churn Prediction
- Technical: Python, scikit-learn, TensorFlow/Keras, OpenCV, SQL, MySQL