compare supervised, unsupervised, and self-supervised paradigms to optimize organizational ML pipelines
Sub Category
- Data Science
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Objectives
- Define machine learning paradigms based on training signals and structural problem formulation.
- Differentiate between generative and discriminative model families for supervised learning tasks.
- Apply advanced evaluation metrics including F1-Score, ROC-AUC, and MAE to imbalanced datasets.
- Implement unsupervised clustering solutions using K-Means, DBSCAN, and Hierarchical methodologies.
- Utilize dimensionality reduction techniques like PCA and t-SNE for high-dimensional data visualization.
- Evaluate the structural mechanics of self-supervised learning and its role in foundation models.
- Design hybrid ML pipelines that chain unsupervised segmentation with localized supervised predictors.
- Navigate the model selection process using a structured six-question business decision framework.
- Mitigate the risks of pattern drift in production environments using unsupervised monitoring tools.
Pre Requisites
- Familiarity with Python programming and the scikit-learn library.
- Foundational understanding of statistics and probability.
- Basic knowledge of data handling and tabular data structures.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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