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所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial-intelligence-3-knn-bayes-classification-method/
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课程名称: 人工智能 #3：k最近邻和贝叶斯分类方法 课程概述: 在本课程中，您将学习 k-最近邻（k-NN）和朴素贝叶斯分类方法。k-最近邻算法是一种非参数分类和回归的方法，属于基于实例的学习，计算只在分类时进行，避免了复杂的训练步骤。通过对邻居的贡献赋予权重，能够更加有效地进行分类。此外，朴素贝叶斯分类器是一种基于贝叶斯定理的简单概率分类器，假设特征之间是独立的，具有很高的可扩展性，训练速度快。 本课程将指导您使用 k-最近邻分类方法来找到数据的正确类别并减少错误，然后进一步学习如何使用朴素贝叶斯分类方法对模型输出进行分类。课程分为两个部分： 第一部分：使用 Python 进行分类，包括 IRIS 花卉数据集，并创建自己的 k 最近邻算法。 第二部分：使用 Python 对非线性结构的输出进行分类，涉及 IRIS 花卉和 Pima 印第安人糖尿病数据库，并创建自己的朴素贝叶斯算法。 重要信息：如果您觉得课程对您的职业生涯没有帮助，可以在30天内申请全额退款。注册后，您将获得无限的终身课程访问权限，并可以随时获得课程更新。如果您在学习过程中遇到任何问题或有建议，我会提供全力支持。 课程大纲和免费预览讲座可以帮助您快速了解课程内容。现在是采取行动的时候，点击 "注册课程" 按钮，开始您的人工智能学习之旅吧！期待在课程中见到您！ 最好的祝愿，Sobhan
In this Course you learn k-Nearest Neighbors & Naive Bayes Classification Methods.In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.For classification, a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. The neighbors are taken from a set of objects for which the class (for k-NN classification). This can be thought of as the training set for the algorithm, though no explicit training step is required.In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.In the statistics and computer science literature, Naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.In this course you learn how to classify datasets by k-Nearest Neighbors Classification Method to find the correct class for data and reduce error. Then you go further You will learn how to classify output of model by using Naive Bayes Classification Method.In the first section you learn how to use python to estimate output of your system. In this section you can classify: Python DatasetIRIS FlowersMake your own k Nearest Neighbors AlgorithmIn the Second section you learn how to use python to classify output of your system with nonlinear structure.In this section you can classify:IRIS FlowersPima Indians Diabetes DatabaseMake your own Naive Bayes Algorithm___________________________________________________________________________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.I 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
