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Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.
Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
