|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-data-science-and-analytics-using-r/
课程评论：没有评论
课程名称：使用R语言的数据科学与分析入门 课程概述：如果您对数据科学和机器学习感兴趣，但尚未接触过这个领域，那么本课程非常适合您！此课程由一位专业数据科学家设计，旨在分享其知识和行业经验，帮助您学习数据科学算法和编码库的基础知识。课程采用逐步的方法来介绍数据科学和机器学习的基本概念。每节课将帮助您建立数学理解和必要库的掌握，助力您顺利通过数据科学面试并进入该领域。 课程结构清晰、全面，以帮助您理解与行业相关的算法。具体分为以下几个部分： 第一部分：R语言入门 - 设置R环境 - 开始使用R Studio和Swirl 第二部分：统计测量介绍 - 中心倾向的测量 - 使用R引入数据科学 第三部分：R中的数据处理与可视化 - 离散度测量与异常值处理 - R中的缺失值处理 - 数据可视化（箱线图、气泡图、热力图、自动化EDA） 第四部分：构建线性回归模型 - 线性回归理论 - 使用R进行线性回归 - 多元线性回归理论与实践 第五部分：构建分类模型 - 使用逻辑回归进行分类 - R中的逻辑回归与广义线性模型，以及分类模型的准确性测量（AIC、AUC、混淆矩阵、精确率和召回率） 第六部分：R中的随机森林模型 - 决策树分类器简介（trees包、基尼指数和树修剪） - 在R中创建决策树和随机森林（随机森林包、超参数调优和树可视化） - 构建随机森林回归模型 课程包含基于真实数据集的实操练习，帮助您动手构建模型。此外，课程还提供可下载的R代码模板，方便您在自己的项目中重复使用。
Are you interested in the field of Data Science and Machine Learning but haven't had experience in it? Then this course is for you!This course has been designed by a professional Data Scientist so that I can share my knowledge and industry experience and help you learn the basics of data science algorithms and coding libraries.This course includes a step-by-step approach to Data Science and Machine Learning. With each lecture, you will develop the mathematical understanding as well as the understanding of necessary libraries to help you ace Data Science interviews and enter into this field. The course is structured in a very crisp and comprehensive manner to help you understand industry-relevant algorithms. It is structured the following way:Part 1.) Getting started with RSetting up RGetting Started with R Studios IDE SwirlPart 2.) Introduction to Statistical MeasuresMeasures of Central TendenciesIntroduction to Data Science using RPart 3.) Data Processing and Data Visualisation in RMeasures of Dispersions and Outlier TreatmentMissing Value Treatment using RData Visualization using R ( boxplots, bubble plots, heat plots, automated-EDA in R)Part 4.) Building Regression Models in RLinear Regression TheoryLinear Regression using RMultivariate Linear Regression TheoryMultivariate Linear Regression using R (Multiple Linear Regression, R-square, Adjusted R-square, p-value, backward selection)Part 5.) Building Classification Models in RClassification using Logistic RegressionLogistic Regression and Generalized Linear Models in R & Measures of Accuracy for a Classification Models (AIC, AUC, Confusion Matrix, Precision, and Recall)Part 6.) Random Forest Models in RIntroduction to decision tree classifier (trees package, Gini index, and tree pruning )Creating decision tree and Random Forest in R (Random forest package in R, hyper-parameters tuning, visualizing a tree in R)Building Random Forest RegressorsThe course takes you through practical exercises that are based on real-life datasets to help you build models hands-on.And as additional material, this course includes R code templates which you can download and re-use on your own projects.
