Supervised Machine Learning: Classification and Regression in R

Hannah将使用两个数据集，一组肿瘤中的乳腺癌检测和另一组孕妇的糖尿病诊断，来解释如何使用四种类型的机器学习模型：K-近邻算法，逻辑回归，决策树和随机森林。
Using two datasets, one for breast cancer detection in tumors and another for diabetes diagnosis in pregnant women, Hannah will explain how to use four types of machine learning models: K-Nearest Neighbors, Logistic Regression, Decision Trees and Random Forests.

这些模型的多功能性和灵活性使其成为功能强大的预测工具，可以简单适应您的专业或学术环境。而我们遇到的许多问题都可以描述为分类任务，本教程将帮助您利用机器学习的力量来理解您的数据并洞悉手头的任务。
The versatility, flexibility and surprising simplicity of these approaches make powerful predictive tools which you can adapt to your own professional or academic environment. Many problems can be described as classification tasks where an outcome either happens or it doesn’t. This tutorial will help you harness the power of machine learning to understand your data and get insight on the task at hand.

本教程将包含监督式机器学习及其方法的描述性概述，以及将这些技术付诸实践的动手编程示例。
The tutorial will contain a descriptive overview of supervised machine learning and its methods, alongside hands-on coding examples for putting these techniques into practice.

该活动面向所有级别的R用户。初学者将对监督式学习的工作原理有一个入门性的了解，而更高级的编码人员将受益于全面的实用数据分析工具包。
Any level experience of R is welcome. Complete beginners will gain an introductory understanding to how supervised machine learning works and more advanced coders will benefit from a comprehensive practical toolkit for data analytics.

无论您是机器学习领域的新手，还是想提高自己的知识水平，都不要错过这次机会！
Whether you’re new to the machine learning field or looking to advance your knowledge, this is your moment!

请扫码或者用以下的链接来报名参加活动！
Please scan the QR code or use the link below to sign up for the event!
http://www.huodongxing.com/event/2551228350722