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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-mining-with-rapidminer/
课程评论：没有评论
Coursera 课程《使用 RapidMiner 进行数据挖掘》全面介绍了数据分析和数据科学的学习价值，强调了解决问题能力、行业高需求、数据分析的广泛应用、其日益增长的重要性以及数据分析师所需的跨学科技能。 本课程将引导您掌握使用 RapidMiner 进行数据挖掘的实用技能，课程遵循 CRISP-DM 数据挖掘流程，涵盖数据理解、数据准备、建模和评估等关键阶段。您将学习如何使用 RapidMiner 加载和处理数据（包括 CSV 文件），进行各种数据可视化（如散点图、折线图、条形图、直方图、箱线图、饼图和散点图矩阵），并掌握必要的数据预处理技术（如归一化、处理缺失值、去重和异常值检测）。 在建模部分，课程将教授如何使用 RapidMiner 实现多种预测模型，包括简单线性回归、K-Means 聚类、凝聚聚类、ID3 决策树、K-NN 分类、朴素贝叶斯分类以及神经网络分类。此外，课程还包含模型评估部分，重点讲解了决策树、K-NN、朴素贝叶斯和神经网络模型的评估方法，并介绍了 K 折交叉验证等技术，帮助您选择合适的算法并评估模型性能。 本课程适合希望通过 RapidMiner 软件提升数据挖掘能力的学习者。
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 Data Mining using RapidmIner. This course uses CRISP-DM data mining process. You will learn RapidMiner to do data understanding, data preparation, modeling, and Evaluation. You will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course. You can take the course as following and you can take an exam at EMHAcademy to get SVBook Advance Certificate in Data Science using DSTK, Excel, and RapidMiner: - Introduction to Data and Text Mining using DSTK 3- Data Mining with RapidMiner- Learn Microsoft Excel Basics Fast- Learn Data analysis using Microsoft Excel Basics Fast. ContentGetting StartedGetting Started 2Data Mining ProcessDownload Data SetRead CSVData Understanding: StatisticsData Understanding: ScatterplotData Understanding: LineData Understanding: BarData Understanding: HistogramData Understanding: BoxPLotData Understanding: PieData Understanding: Scatterplot MatrixData Preparation: NormalizationData Preparation: Replace Missing ValuesData Preparation: Remove DuplicatesData Preparation: Detect OutlierModeling: Simple Linear RegressionModeling: Simple Linear Regression using RapidMinerModeling: KMeans CLusteringModeling: KMeans Clustering using RapidmInerModeling: Agglomeration CLusteringModeling: Agglomeration Clustering using RapidmInerModeling: Decision Tree ID3 AlgorithmModeling: Decision Tree ID3 Algorithm using RapdimInerModeling: Decision Tree ID3 Algorithm using RapidMinerEvaluation: Decision Tree ID3 Algorithm using RapidmInerModeling: KNN ClassificationModeling: KNN CLassification using RapidmInerEvaluation: KNN Classification using RapidmInerModeling Naive Bayes ClassificationModeling: Naive Bayes Classification using RapidmInerEvaluation: Naive Bayes Classification using RapidMInerModeling: Neural Network ClassificationModeling: Neural Network Classification using RapidmInerEvaluation: Neural Network Classification using RapidmInerWhat Algorithm to USe?Model Evaluationk fold cross-validation using RapdimIner
