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This article is part of “AI education”, a series of posts that review and explore educational content on data science and machine learning. (In partnership with Paperspace)
In this post, I will introduce some of my favorite machine learning math resources, suggest Ben Dickson, software engineer and the founder of TechTalks
And while I don’t expect you to have fun with machine
learning math, I will also try my best to give you some guidelines on
how to make the journey a bit more pleasant.
Mathematics of machine learning
The mathematics of machine learning is complicated. But it can become pleasant if you know where to start your learning journey.
How much math knowledge do you need for machine learning and deep learning? Some people say not much. Others say a lot. Both are correct, depending on what you want to achieve.
There are plenty of programming libraries, code snippets, and pretrained models that can get help you integrate machine learning into your applications without having a deep knowledge of the underlying math functions...
When should you learn machine learning mathematics?
Agreeably, mathematics is not the most fun way to start machine learning education, especially if you’re self-learning. Fortunately, as I said at the beginning of this article, you don’t need to begin your machine learning education by poring over double integrals, partial derivatives, and mathematical equations that span a page’s width.
You can start with some of the more practical resources on data science and machine learning.
Source: TechTalks
