Hidden Bibliographic Details
| Varying Form of Title: | Introduction to real-world machine learning
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| Other authors / contributors: | Ma, Angie, speaker.
Willis, Gary, speaker.
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| Notes: | Title from title screen (viewed September 21, 2017).
Date of publication taken from resource description page.
"Part 3 of 6."
Presenter, Alessandra Staglianò, Angie Ma, and Gary Willis.
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| Summary: | "Classification is the sub-field of machine learning encountered more frequently than any other in data science applications. There are many different classification techniques and this course explains some of the most important ones, including algorithms such as logistic regression, k-nearest neighbors (k-NN), decision trees, ensemble models like random forests, and support vector machines. The course also covers Naive Bayes classifiers and in so doing, covers Bayes' theorem and basic Bayesian inference, both of which are widely used in training many machine learning algorithms. A basic knowledge of algebra is required. A solid understanding of differential calculus will be necessary for logistic regression, Support Vector Machines and Bayesian Inference."--Resource description page
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