Description: An essential course for understanding how machines learn from data, covering core machine learning concepts, algorithms, and applications.
Topics Covered: Supervised and unsupervised learning, key algorithms (linear regression, decision trees, K-means), model evaluation metrics, and data preprocessing.
Ideal For: Beginners to intermediate learners interested in building a foundation in machine learning.