A Quick Guide to Data Mining with Weka and Java using Weka

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This technical book aim to equip the reader with Weka, Data Mining in a fast and practical way. There will be many examples and explanations that are straight to the point.á


1. Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data)á

2. Getting Started (INstall Weka)á

3. Prediction and Classification (Prediction and Classification)á

4. Machine Learning Basics (KMeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network)á

5. Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka)á

6. Java interact Weka (Use Java to use Weka, in order to develop your own prediction or classification system)á

7. Conclusion

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

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Introduction 2 Getting Started
Prediction and Classification
Machine Learning Basics
Data Mining with Weka
Java and Weka

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About the author

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.

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