Hit Pause on AI: Time to Refresh the Basics
Last Updated on November 3, 2024 by Editorial Team
Author(s): Elangoraj Thiruppandiaraj
Originally published on Towards AI.
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Artificial Intelligence (AI) has quickly evolved from a buzzword to an important component in almost all industries today. Itβs easy to get caught up in complex algorithms and fancy results it produces. However, itβs not good to overlook the importance of foundations or basics (especially statistics), which are the backbone of Generative AI and Data Science as a whole.
For any new starter to the field, the temptation is to move straight to model building and applying generative AI, but without a solid understanding of statistical principles, most of the projects are likely to fail. As an experienced Data Scientist, I or any expert will talk about the importance of statistics and it is a core foundation in understanding data, validating models, and making informed decisions.
In this article, I will revisit the statistical foundations that can help you become an industry-ready AI practitioner. Three topics in particular: descriptive statistics, probability theory, and inferential statistics. This can refine your skills and ensure that your AI models are not just sophisticated but statistically sound.
Source: Dale EFirst step in Data Science project is understanding of your dataset. Descriptive statistics help us provide simple… Read the full blog for free on Medium.
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