A Quick Guide to Data Mining using RapidMiner and Weka
A Quick Guide to Data Mining using RapidMiner and Weka
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A Quick Guide to Data Mining using RapidMiner and Weka

This book is 100% complete

Completed on 2019-05-15

About the Book

This book aim to equip the reader with RaidMiner and Weka and Data Mining basics. There will be many examples and explanations that are straight to the point. You will be walked through data mining process from data preparation to data analysis (descriptive statistics) and data visualization to prediction modeling (machine learning) using Weka and RapidMiner.

Content Covered:

  • Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data)
  • Getting Started (INstall Weka and RapidMiner)
  • Prediction and Classification (Prediction and Classification)
  • Machine Learning Basics (Kmeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network)
  • Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka)
  • Data Mining with RapidMiner (Data Understanding using RapidMiner, Data Preparation using RapidMiner, Model Building and Evaluation using RapidMiner)
  • Conclusion

We will be using opensource tools, hence, you don't have to worry about buying any softwares. The book is designed for non-programmers only. It will gives you a head start into Weka and RapidMiner, with a touch on data mining.

This book has been taught at Udemy and EMHAcademy.com.

Use the following Coupon to get the Udemy Course at $11.99:

https://www.udemy.com/data-mining-with-rapidminer/?couponCode=EBOOKSPECIAL

https://www.udemy.com/learn-machine-learning-with-weka/?couponCode=EBOOKSPECIAL

About the Author

Eric Goh
Eric Goh

Eric Goh is a data scientist, software engineer, adjunct faculty and entrepreneur with years of experiences in multiple industries. His varied career includes data science, data and text mining, natural language processing, machine learning, intelligent system development, and engineering product design. He founded SVBook Pte. Ltd. and extended it with DSTK.Tech and EMHAcademy.com. DSTK.Tech is where Eric develops his own DSTK data science softwares (public version). Eric also published “Learn R for Applied Statistics” at Apress, and published some books at LeanPub and SVBook Pte. Ltd. He teaches the content at Udemy and EMHAcademy.com, and developed 28 courses, 7 advanced certificates. Eric is also an adjunct faculty at Universities and Institutions, which is a consultancy from EMHAcademy.com.

Eric Goh has been leading his teams for various industrial projects, including the advanced product code classification system project which automates Singapore Custom’s trade facilitation process, and Nanyang Technological University's data science projects where he develop his own DSTK data science software. He has years of experience in C#, Java, C/C++, SPSS Statistics and Modeller, SAS Enterprise Miner, R, Python, Excel, Excel VBA and etc. He won Tan Kah Kee Young Inventors' Merit Award and Shortlisted Entry for TelR Data Mining Challenge.

Eric holds a Masters of Technology degree from the National University of Singapore, an Executive MBA degree from U21Global (currently GlobalNxt) and IGNOU, a Graduate Diploma in Mechatronics from A*STAR SIMTech (a national research institute located in Nanyang Technological University), Coursera Specialization Certificate in Business Statistics and Analysis (Excel) from Rice University, IBM Data Science Professional Certificate (Python, SQL), and Coursera Verified Certificate in R Programming from Johns Hopkins University. He possessed a Bachelor of Science degree in Computing from the University of Portsmouth after National Service. He is also an AIIM Certified Business Process Management Master (BPMM), GSTF certified Big Data Science Analyst (CBDSA), and IES Certified Lecturer.

Table of Contents

  • Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data)
  • Getting Started (INstall Weka and RapidMiner)
  • Prediction and Classification (Prediction and Classification)
  • Machine Learning Basics (Kmeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network)
  • Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka)
  • Data Mining with RapidMiner (Data Understanding using RapidMiner, Data Preparation using RapidMiner, Model Building and Evaluation using RapidMiner)
  • Conclusion

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