The Theoretical Basis of Machine Learning

Machine learning is a big topic. Before you can start to use it, you need to understand what it is, and what it is and isn’t capable of. This lesson is part two of the module on machine learning. It covers unsupervised learning, the theoretical basis for machine learning, model and linear regression, the semantic gap, and how we approximate the truth. 

Part one of this two-part series can be found here, and covers the history and ethics of AI, data, statistics and variables, notation, and supervised learning.

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