#60: Scikit-learn 57:Supervised Learning 35: Kernels and Kernel Ridge regression

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The video discusses the code for kernels linear, polynomial, sigmoid and RBF and implementation of Kernel Ridge Regression using Scikit learn in Python.

Timeline
(Python 3.8)

00:00 - Outline of video
00:57 - Open Jupyter notebook
01:30 - Add dimension to a design matrix
05:46 - Add dimension: create 2D, 3D plot
07:13 - * * * CORRECTION * * *: it should be '_' i.e. underscore instead of '-'
10:20 - Compare fit: after adding dimension
12:32 - Linear kernel
13:52 - Polynomial kernel
14:33 - * * * CORRECTION * * *: forgot to add the '=' sign as 'k='
17:06 - RBF (radial basis function) kernel
20:15 - Sigmoid kernel
22:20 - Custom kernel
24:58 - Fit: Ridge classifier
27:26 - Fit: Kernel Ridge regression
29:10 - Fit: KernelRidge: kernel='precomputed'
33:10 - Ending notes
  1. learndataa
  2. kernel
  3. KernelRidge
  4. kernel ridge regression
  5. linear kernel
  6. polynomial kernel
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