Introductory Applied Machine Learning
Introductory Applied Machine Learning
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From Nigel Goddard on October 1st, 2017
Feature Transforms and Radial Basis Functions -
From Nigel Goddard on October 1st, 2017
Diagnosing problems and dealing with multiple output values -
From Nigel Goddard on October 1st, 2017
A probabilistic formulation of the linear model -
From Nigel Goddard on October 1st, 2017
How we fit parameters for the linear model -
From Nigel Goddard on October 1st, 2017
The Linear Model and Examples -
From Nigel Goddard on November 12th, 2016
Preliminaries - perceptrons -
From Nigel Goddard on October 9th, 2016
Linear Regression Summary -
From Nigel Goddard on October 9th, 2016
Feature Transformation -
From Nigel Goddard on October 9th, 2016
Basis Expansion -
From Nigel Goddard on October 9th, 2016
Multiple Regression -
From Nigel Goddard on October 9th, 2016
A Probabilistic View -
From Nigel Goddard on October 9th, 2016
Problems with Outliers -
From Nigel Goddard on October 9th, 2016
Fitting the Linear Model to Data -
From Nigel Goddard on October 9th, 2016
Linear Algebra Formulation -
From Nigel Goddard on October 9th, 2016
Linear Regression Examples
