Machine Learning Fundamentals
About Course
An introduction to building predictive models using supervised and unsupervised ML techniques.
Course Content
Overview of ML workflows (train/test split)
Classification (decision trees, random forests, logistic regression)
Clustering (k-means, hierarchical clustering)
Model evaluation metrics (accuracy, ROC, confusion matrix)
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