Mathematics for Machine Learning
Mathematics for Machine Learning

An online book that provides foundational knowledge about the mathematics behind machine learning concepts.

A new online resource appeared this week which gained much attention on Twitter. Just take a look at the engagement below:

Not everyday a resource like this comes by, actually this project, according to the authors, took roughly two years to complete. I thought it was a good idea to share it with our community. This will be archived under the “Learn” tab of this publication, which is intended to highlight useful educational resources and material to learn about concepts related to machine learning, artificial intelligence, natural language processing, and deep learning.

The resource I am referring to is the online book called “Mathematics for Machine Learning” by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. I share this book here because it’s free and it contains a great introduction to the mathematics behind some of the most pervasive machine learning techniques. Some concepts include Analytic Geometry, Vector Calculus, and Continuous Optimization, among others. The summarized table of contents looks as follows:

If you ever need a place to start learning about the maths behind machine learning, then this a highly recommended book.

Besides the book itself, the open project also provides easy to follow python notebooks that include code walkthrough of concepts like Maximum Likelihood and PCA, which are all important techniques in machine learning.
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['Machine Learning', 'Technology', 'Artificial Intelligence', 'Data Science', 'Deep Learning']