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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-r-d/
课程评论：没有评论
R语言机器学习课程，旨在教授数据分析和数据科学的基础知识及实践技能。课程强调学习数据分析的五大原因：提升解决问题的能力、应对高需求领域、认识到分析无处不在、理解其日益增长的重要性以及掌握跨领域的综合技能。 本课程将R语言编程应用于机器学习和统计学习，聚焦于CRISP-DM数据挖掘流程中的建模和评估阶段。学员将学习如何使用R构建和评估预测模型，包括朴素贝叶斯、决策树、K近邻(KNN)、神经网络和线性回归等算法。 课程内容涵盖： * R语言基础（已在“创建你的计算器：快速学习R语言编程”课程中讲解） * 数据预处理与理解（结合“R语言应用统计及数据处理”和“R语言高级数据可视化及数据处理”课程） * 线性回归模型的构建与预测 * K-Means聚类与凝聚聚类 * 决策树（ID3算法）的实现与应用 * K近邻(KNN)分类的训练与预测 * 朴素贝叶斯分类的R语言实践 * 神经网络分类的应用 * 算法选择指南 * 模型评估方法（分类与回归） 完成课程后，学员将能通过EMHAcademy的考试，获得SVBook认证数据挖掘师（使用R）证书。
Why learn Data Analysis and Data Science?According to SAS, the five reasons are1. Gain problem solving skillsThe ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life. 2. High demandData Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase. 3. Analytics is everywhereData is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.4. It's only becoming more importantWith the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities. 5. A range of related skillsThe great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths. Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities. This is the bite-size course to learn R Programming for Machine Learning and Statistical Learning. In CRISP-DM data mining process, machine learning is at the modeling and evaluation stage. You will need to know some R programming, and you can learn R programming from my "Create Your Calculator: Learn R Programming Basics Fast" course. You will learn R Programming for machine learning and you will be able to train your own prediction models with Naive Bayes, decision trees, knn, neural network, and linear regression, and evaluate your models very soon after learning the course. You can take the course as follows, and you can take an exam at EMHAcademy to get SVBook Certified Data Miner using R certificate: - Create Your Calculator: Learn R Programming Basics Fast (R Basics)- Applied Statistics using R with Data Processing (Data Understanding and Data Preparation)- Advanced Data Visualizations using R with Data Processing (Data Understanding and Data Preparation, in the future)- Machine Learning with R (Modeling and Evaluation)ContentGetting StartedGetting Started 2Getting Started 3Data Mining ProcessDownload Data setRead Data setSome ExplanationsSimple Linear RegressionBuild Linear Regression ModelsPredict Linear Regression ModelsKMeans ClusteringKMeans Clustering in RAgglomeration ClusteringAgglomeration Clustering in RDecision Tree ID3 AlgorithmDecision Tree in R: Split train and test setDecision Tree in R: Train Decision TreeDecision Tree in R: Predict Decision TreeKNN ClassificationTrain KNN in RPredict KNN in RNaive Bayes ClassificationNaive Bayes in RNeural Network ClassificationNeural Network in RWhat Algorithm to Use?Model EvaluationModel Evaluation using R for ClassificationModel Evaluation using R for Regression
