Basics for Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

Some classical methods used in the field of linear algebra,such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. This book helps machine learning practitioners, get on top of linear algebra, fast.

Author(s): Jason Brownlee
Edition: 1.1
Year: 2018

Language: English
Commentary: LaTeX with hyperref package
Pages: 0
Tags: Linear Algebra, Machine Learning

i. Introduction
ii. Foundations of Linear Algebra
iii. NumPy
iv. Matrices
v. Factorization
vi. Statistics
vii. Appendix