Introductory Applied Machine Learning
Introductory Applied Machine Learning
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From Nigel Goddard
Feature Transforms and Radial Basis Functions -
From Nigel Goddard
Diagnosing problems and dealing with multiple output values -
From Nigel Goddard
A probabilistic formulation of the linear model -
From Nigel Goddard
How we fit parameters for the linear model -
From Nigel Goddard
The Linear Model and Examples -
From Nigel Goddard
Preliminaries - perceptrons -
From Nigel Goddard
Linear Regression Summary -
From Nigel Goddard
Feature Transformation -
From Nigel Goddard
Basis Expansion -
From Nigel Goddard
Multiple Regression -
From Nigel Goddard
A Probabilistic View -
From Nigel Goddard
Problems with Outliers -
From Nigel Goddard
Fitting the Linear Model to Data -
From Nigel Goddard
Linear Algebra Formulation -
From Nigel Goddard
Linear Regression Examples
