newsletter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"What math subjects are used in machine learning, and how are they used? In this research paper by Richard Han, Ph.D., we look at the mathematics behind the machine learning techniques linear regression, linear discriminant analysis, logistic regression, artificial neural networks, and support vector machines.<\/p>\n","protected":false},"author":10513,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[87,180,67,56,1],"tags":[277,705,96],"acf":[],"yoast_head":"\n
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I am your host Daniel Gutierrez from insideBIGDATA where I serve as Editor-in-Chief & Resident Data Scientist. Today\u2019s topic is \u201cThe Math Behind the Models,\u201d one of my\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2023\/06\/Power-Data-column-banner_special.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":26819,"url":"https:\/\/insidebigdata.com\/2021\/08\/04\/book-review-mathematics-for-machine-learning\/","url_meta":{"origin":21430,"position":1},"title":"Book Review: Mathematics for Machine Learning","date":"August 4, 2021","format":false,"excerpt":"\"Mathematics for Machine Learning\" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press, is an excellent way to learn the math behind the models. This review shall highlight all the ways this book is special among the competition. Of all the books I've\u2026","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2021\/07\/Mathematics-ML-book_gradient.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":16560,"url":"https:\/\/insidebigdata.com\/2016\/11\/26\/the-math-behind-basketballs-wildest-moves\/","url_meta":{"origin":21430,"position":2},"title":"The Math Behind Basketball\u2019s Wildest Moves","date":"November 26, 2016","format":false,"excerpt":"If basketball is of more interest to you than business intelligence, you\u2019ll like this TED talk by Rajiv Maheswaran. If you want pithy examples of what algorithms and machine learning are, you\u2019ll like Maheswaran\u2019s talk even more. 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This book is a must-have for any serious data scientist.","rel":"","context":"In "Book Review"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":12640,"url":"https:\/\/insidebigdata.com\/2015\/01\/23\/data-science-101-random-forests\/","url_meta":{"origin":21430,"position":4},"title":"Data Science 101: Random Forests","date":"January 23, 2015","format":false,"excerpt":"The Random forests machine learning algorithm is a popular ensemble method used by many data scientists to achieve good predictive performance in the classification regime. Fully understanding the nuances of this statistical learning technique is paramount to getting the most out of this algorithm - unfortunately, this means math. 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Machine learning is a wildly popular field of technology that is being used by data scientists around the globe. Mastering machine learning can be achieved via many avenues of study, but one arguably necessary ingredient to success is a fundamental understanding of the mathematics behind the algorithms. Some data scientists-in-training often try to take a shortcut and bypass the math, but that route is shortsighted. In order to get the most out of machine learning, you really need that important perspective for what the algorithm is really doing behind the scenes. This perspective is only available with the math. Ever wonder what those tuning parameters actually do? The math has all the answers.<\/p>\n![\"\"](\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/11\/Richard-Han.jpg\")