Badge Overview

Deep Learning / Data Mining  Deep Learning / Data Mining

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Deep Learning / Data Mining

Deep Learning / Data Mining

Issued by Hood College

Badge Description

The Deep Learning badge provides computer science learners with a foundational understanding of data analysis and knowledge discovery, introducing both the theory and application of modern machine learning methods. The course covers essential topics such as data preprocessing for preparing high-quality inputs, association rule learning for uncovering hidden patterns, classification techniques for predictive modeling and anomaly detection for identifying irregularities in data. Learners gain practical experience in building and training models, applying algorithms to real-world datasets and evaluating performance using key metrics.

Skills Deep Learning Artificial Intelligence Computer Vision Natural Language Processing (NLP) Machine Learning Frameworks

Badge Criteria

This ADVANCED level badge is equivalent to a 3-credit, master's-level course. Earning a grade of B or better is required for this badge.

1. Design, implement and train deep learning models, including CNNs, RNNs and Transformers. 2. Use frameworks like TensorFlow and PyTorch to build and optimize models. 3. Process large datasets on cloud platforms (AWS, Google Cloud) or high-performance computing systems. 4. Evaluate model performance using metrics (accuracy, precision-recall) and debugging techniques (e.g., overfitting, vanishing gradients). 5. Apply model explainability tools (e.g., SHAP, LIME) to interpret predictions. 6. Build solutions for computer vision tasks (image classification, object detection) and NLP tasks (text summarization, translation). 7. Develop an end-to-end project portfolio showcasing data preprocessing, training and deployment of deep learning models. 8. Analyze deep learning research papers to understand state-of-the-art advancements. 9. Address ethical challenges in AI, including bias, fairness and privacy concerns. 10. Deploy models at scale using Docker/Kubernetes and integrate cloud resources. 11. Collaborate on team projects and apply professional tools such as Git and version control.

Aligned Outcomes