: Using Tom M. Mitchell’s framework, an algorithm learns from a Task (T) , through Experience (E) , measured by a Performance (P) metric. 2. The Three Pillars of Learning

: Measuring accuracy and pushing the model to a real-world environment. 4. Essential Tools and Skills

: Models find hidden patterns in "unlabeled" data without prior guidance (e.g., customer segmentation).

: An agent learns through trial and error, receiving rewards for good actions and penalties for bad ones (e.g., AI playing video games). 3. The Machine Learning Workflow

: Telling the "student" (the algorithm) to find the best-fit line or relationship in the data.