- Building and Implementing Data Pipelines for ML Projects (k4)
Students can create data preparation and preprocessing pipelines, including data collection, cleaning, augmentation, and loading for machine learning tasks.
- Designing and Implementing Neural Networks in PyTorch (k4)
Students are able to design, implement, and test neural network architectures using PyTorch, handling both inference and training processes effectively.
- Applying Model Training Techniques (k4)
Students can execute model training procedures, implementing various training techniques and optimizing hyperparameters to enhance neural network performance.
- Evaluating and Analyzing Model Performance (k5)
Students can critically evaluate the performance of machine learning models, interpreting metrics and results to improve model accuracy and generalization.
- Managing Medium-Sized ML Projects (k6)
Students are capable of independently planning and managing a medium-sized ML project from start to finish, integrating all stages including data preparation, model development, training, and performance evaluation.
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Students have in-depth knowledge of implementing machine learning workflows in Python, specifically using PyTorch, covering data preparation, neural network design, training, and evaluation. They understand the practical challenges and best practices of end-to-end machine learning projects, enabling them to apply programming skills effectively for complex scientific applications.
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