# Computational Linear Algebra for Coders

Other,, Summer 2017 , Prof. Rachel Thomas

Updated On 02 Feb, 19

Other,, Summer 2017 , Prof. Rachel Thomas

Updated On 02 Feb, 19

This course is focused on the question: How do we do matrix computations with acceptable speed and acceptable accuracy? The course is taught in Python with Jupyter Notebooks, using libraries such as scikit-learn and numpy for most lessons, as well as numba and pytorch in a few lessons.

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4.1 ( 11 )

Predicting health outcomes on a diabetes data set with least squares linear regression:

- Linear regression in sklearn

- Polynomial Features

- Speeding up with Numba

- Regularization and Noise

How to implement linear regression yourself:

- How did Scikit Learn do it?

- Naive solution

- Normal equations and Cholesky factorization

- QR factorization

- SVD

- Timing Comparison

Sam

Sep 12, 2018

Excellent course helped me understand topic that i couldn't while attendinfg my college.

Dembe

March 29, 2019

Great course. Thank you very much.