Machine Learning Internship

Selected intern's day-to-day responsibilities include:

1. Data collection & preprocessing: Gather, clean, and preprocess datasets for model training.
2. Model development & training: Build, train, and fine-tune machine learning models for various tasks.
3. Feature engineering: Extract meaningful features to improve model accuracy and performance.
4. Hyperparameter tuning: Optimize ML models using techniques like Grid Search, Random Search, or Bayesian Optimization.
5. Algorithm implementation: Work on regression, classification, clustering, and deep learning models.
6. Performance evaluation: Test and validate models using metrics like accuracy, precision, recall, and F1-score.
7. Research & experimentation: Explore the latest ML advancements, frameworks, and best practices.
8. Collaboration with teams: Work with data scientists and software engineers to integrate models into applications.
9. Model deployment (if required): Assist in deploying ML models on cloud platforms (AWS, GCP, Azure) or edge devices.
10. Documentation & reporting: Maintain well-documented code, summarize findings, and present insights.

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