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Jason Brownlee
Machine Learning Mastery With Weka
Analyze Data, Develop Models and Work Through Projects
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Machine Learning Mastery With Weka
©Copyright 2016 Jason Brownlee. All Rights Reserved.
Edition: v1.1
Contents
I Introduction
1 Welcome 2
1.1 Applied Machine Learning the Wrong Way
1.2 Applied Machine Learning with Weka
1.3 Book Overview
1.4 Your Outcomes From This Process
1.5 What This Book is Not
1.6 Summary
2 Rapidly Accelerate Your Progress in Applied Machine Learning With Weka
2.1 Starting in Applied Machine Learning is Hard
2.2 Focus on Learning Just One Thing
2.3 Learn the Process of Applied Machine Learning
2.4 How to Best Use Weka
2.5 Summary
3 A Gentle Introduction to the Weka Machine Learning Workbench
3.1 What is Weka
3.2 Introduction to the Weka Graphical Interface
3.3 Why You Should Use Weka
3.4 Summary
4 How to Make Best Use of Weka For Applied Machine Learning
4.1 Harness The Number One Bene t of Weka
4.2 Build a Machine Learning Portfolio
4.3 Practice On Small In-Memory Datasets
4.4 Bene ts of the Repository
4.5 Summary
II Lessons
5 How to Download and Install the Weka Machine Learning Workbench
5.1 Download Weka
5.2 Install The All-In-One Version of Weka
5.3 Install Java and Weka Separately
5.4 Install Weka On Linux And Other Platforms
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5.5 Summary
6 A Tour of the Weka Machine Learning Workbench
6.1 Weka GUI Chooser
6.2 Weka Explorer
6.3 Weka Experiment Environment
6.4 Weka KnowledgeFlow Environment
6.5 Weka Workbench
6.6 Weka SimpleCLI
6.7 Weka Java API
6.8 Summary
7 How To Load CSV Machine Learning Data
7.1 How to Talk About Data in Weka
7.2 Data in Weka
7.3 Load CSV Files in theARFF-Viewer. . . . . . . . . . . . . . . . . . . . . . . .
7.4 Load CSV Files in theWeka Explorer. . . . . . . . . . . . . . . . . . . . . . .
7.5 Use Excel for Other File Formats
7.6 Summary
8 How to Load Standard Machine Learning Datasets
8.1
Machine learning mastery with Weka_Jason Brownlee.pdf