Intro to Machine Learning for Analysts

Summary

A conceptual overview of what ML models are (pattern-matching functions trained on data) and where analysts fit in — usually preparing data and validating outputs, not building models from scratch.

• 1Read a high-level explainer distinguishing supervised, unsupervised, and reinforcement learning.
• 2Identify what a data scientist would need from you to build a model well.
• 3Learn to critically evaluate a model's output, not just accept it as ground truth.