The purpose of this article and further ones in the “ML 101” series is to discuss the fundamental concepts of Machine Learning. I want to ensure that all the concepts I might use in the future are clearly defined and explained. One of the most significant issues with the adoption of Machine Learning into the field of finance is the concept of a “black box.” There’s a lot of ambiguity about what happens behind the scenes in many of these models, so I am hoping that, in taking a deep-dive into the theory, we can help dispel some worries we might have about trusting these models.
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ML 101 - An Introduction
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The purpose of this article and further ones in the “ML 101” series is to discuss the fundamental concepts of Machine Learning. I want to ensure that all the concepts I might use in the future are clearly defined and explained. One of the most significant issues with the adoption of Machine Learning into the field of finance is the concept of a “black box.” There’s a lot of ambiguity about what happens behind the scenes in many of these models, so I am hoping that, in taking a deep-dive into the theory, we can help dispel some worries we might have about trusting these models.