Data Science and Machine Learning

Currently Playing: Ep 20 — Machine Learning Outliers The Book That Doesn't Belong (Outliers)

#machinelearning #datascience #ml #ai #maths #algortihm #artificialintelligence Core Idea: Anomalies can be errors — or the most important data point in the room. Concept: Outlier (Anomaly) detection. Why it matters: Outliers can severely distort means, models, and conclusions if left unexamined (a perfect callback to Ep 3). Core mechanic: You will demonstrate the Z-score threshold method: or mention the IQR (Interquartile Range) method. Example: A boxplot drawn on screen with a clear, distinct point sitting far beyond the whiskers. Common mistake: Automatically deleting every outlier without investigating why it is there. The Rule: "Not every anomaly is an error. Some are the entire point." Next Clue: Two shelves that always seem to shift together (bridging to Ep 21: Correlation vs. Causation).