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所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial-intelligence-4-svm-logistic-classifier-methods/
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课程名称：人工智能 #4：支持向量机与逻辑回归分类方法 课程概述： 在本课程中，您将学习支持向量机（SVM）和逻辑回归分类方法。支持向量机是一种监督学习模型，适用于分类和回归分析。课程将介绍如何通过SVM算法处理训练示例，并建立模型以将新示例归类为两个类别之一。SVM的核心在于找到一个尽可能宽的间隔，将不同类别的示例在空间中分开，同时还能使用所谓的“核技巧”高效地进行非线性分类。 逻辑回归（Logistic Regression）是一种回归模型，适用于分类因变量（DV）为二元的情况，即输出只能取值“0”和“1”。这类模型在经济学中被视为定性响应/离散选择模型，能够估算基于一个或多个自变量的二元响应的概率。 课程内容： 您将学习如何利用支持向量机对数据集进行分类，以找到正确类别并减少误差。接着，您将使用逻辑回归对模型的输出进行分类。在第一部分，您将学习如何使用Python估算系统的输出，示例包括随机数据集、鸢尾花数据集和手写数字。第二部分则讲授如何使用Python对具有非线性结构的系统输出进行分类，示例包括带状数据、鸢尾花数据集和手写数字。 重要信息： 若您认为该课程对您的职业生涯毫无帮助，您可以在30天内申请退款，确保无理由全额退款！一旦注册，您将获得无限期的终身访问权限，并可随时获取课程更新。我将提供全力支持，以解决您在学习过程中遇到的任何问题或建议。 建议您查看课程大纲和免费试听讲座，了解课程的更多信息。 现在就采取行动吧！点击右上角的“立即参加此课程”按钮，不要浪费时间！每一秒都是宝贵的。我期待在课程中见到您！ 最诚挚的问候，Sobhan
In this Course you learn Support Vector Machine & Logistic Classification Methods. In machine learning, Support Vector Machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. In statistics, Logistic Regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. This article covers the case of a binary dependent variable-that is, where the output can take only two values, "0" and "1", which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has more than two outcome categories may be analysed in multinomial logistic regression, or, if the multiple categories are ordered, in ordinal logistic regression. In the terminology of economics, logistic regression is an example of a qualitative response/discrete choice model. Logistic Regression was developed by statistician David Cox in 1958. The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). It allows one to say that the presence of a risk factor increases the odds of a given outcome by a specific factor. In this course you learn how to classify datasets by by Support Vector Machines to find the correct class for data and reduce error. Next you go further You will learn how to classify output of model by using Logistic Regression In the first section you learn how to use python to estimate output of your system. In this section you can estimate output of: Random dataset IRIS Flowers Handwritten Digits In the Second section you learn how to use python to classify output of your system with nonlinear structure.In this section you can estimate output of: Blobs IRIS Flowers Handwritten Digits ___________________________________________________________________________ Important information before you enroll: In case you find the course useless for your career, don't forget you are covered by a 30 day money back guarantee, full refund, no questions asked! Once enrolled, you have unlimited, lifetime access to the course! You will have instant and free access to any updates I'll add to the course. You will give you my full support regarding any issues or suggestions related to the course. Check out the curriculum and FREE PREVIEW lectures for a quick insight. ___________________________________________________________________________ It's time to take Action! Click the "Take This Course" button at the top right now! ...Don't waste time! Every second of every day is valuable. I can't wait to see you in the course! Best Regrads, Sobhan
