Assumptions of Linear Regression Models

Looking for a short primer on Machine Learning concepts? SDS Founder Kirill Eremenko and AI expert Hadelin de Ponteves are back, joining Jon Krohn to review essential ML concepts. From classification errors to logistic regression, feature scaling, the elbow method and more. The popular data science instructors also introduce their latest course: Machine Learning in Python: Level 1.

In this episode you will learn:
• Kirill and Hadelin's new course [17:34]
• Supervised vs unsupervised learning [26:23]
• False positives and false negatives [31:21]
• Logistic regression [43:00]
• Holding out a set of test data [46:39]
• Feature scaling [52:45]
• The Adjusted R-Squared metric [59:44]
• The five assumptions of linear regression [1:05:12]
• The Elbow Method [1:11:41]

Additional materials: www.superdatascience.com/649

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