The Detailed Math Behind a Supervised Learning Algorithm
Effective Machine Learning Programming Requires Advanced Mathematics, Proven Experience, Creative Problem Solving, and More.
To understand the detailed mathematics behind machine learning algorithms, you need to be familiar with a few core concepts related to:
- Linear algebra
- Vectors
- Matrix multiplication
- Determining the trace, determinant, and transpose of matrices.
- Calculus
If you’ve taken undergraduate courses covering linear algebra and multivariable calculus, you should have a good foundation of knowledge you can apply to machine learning algorithms.
If you don’t have an advanced background in math, you may want to learn more about how software is developed using machine learning algorithms or take a look at how machine learning works. Below, we also have a collapsible in-depth review of important linear algebra concepts related to building a supervised learning algorithm in case you just need a refresher.
Reference: An In-Depth Review of Linear Algebra
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In future articles, we’ll cover some iterative methods we can use to find w and that are also useful when working with models besides linear ones. We’ll also discuss techniques for determining how good of an approximation g really is for our target function. You can check back on our site in the coming weeks or contact us to let us know you want to be added to our exclusive newsletter.
Stratus Innovations Group: Machine Learning Experts
While the math behind machine learning can be abstract and complicated, the problems it solves are concrete, and its results can have a meaningful impact on your business’ bottom line. From predictive maintenance to business analytics and beyond, the machine learning experts at Stratus Innovations Group can help you develop a customized cloud-based solution to solve your unique business challenges.
Learn more by calling 844-561-6721 or by filling out a simple form to contact us. We’ll help you bring the power of the cloud — and machine learning — to your operations!