Machine Learning Mastery With Weka: Analyze Data, Develop Models and Work Through Projects

I Introduction
Welcome
Applied Machine Learning the Wrong Way
Applied Machine Learning with Weka
Book Overview
Your Outcomes From This Process
What This Book is Not
Summary
Rapidly Accelerate Your Progress in Applied Machine Learning With Weka
Starting in Applied Machine Learning is Hard
Focus on Learning Just One Thing
Learn the Process of Applied Machine Learning
How to Best Use Weka
Summary
A Gentle Introduction to the Weka Machine Learning Workbench
What is Weka
Introduction to the Weka Graphical Interface
Why You Should Use Weka
Summary
How to Make Best Use of Weka For Applied Machine Learning
Harness The Number One Benefit of Weka
Build a Machine Learning Portfolio
Practice On Small In-Memory Datasets
Benefits of the Repository
Summary
II Lessons
How to Download and Install the Weka Machine Learning Workbench
Download Weka
Install The All-In-One Version of Weka
Install Java and Weka Separately
Install Weka On Linux And Other Platforms
Summary
A Tour of the Weka Machine Learning Workbench
Weka GUI Chooser
Weka Explorer
Weka Experiment Environment
Weka KnowledgeFlow Environment
Weka Workbench
Weka SimpleCLI
Weka Java API
Summary
How To Load CSV Machine Learning Data
How to Talk About Data in Weka
Data in Weka
Load CSV Files in the ARFF-Viewer
Load CSV Files in the Weka Explorer
Use Excel for Other File Formats
Summary
How to Load Standard Machine Learning Datasets
Standard Weka Datasets
Binary Classification Datasets
Multiclass Classification Datasets
Regression Datasets
Summary
How to Better Understand Your Machine Learning Data
Descriptive Statistics
Univariate Attribute Distributions
Visualize Attribute Interactions
Summary
How to Normalize and Standardize Your Machine Learning Data
About Data Filters in Weka
Normalize Your Numeric Attributes
Standardize Your Numeric Attributes
Summary
How to Transform Your Machine Learning Data
Discretize Numerical Attributes
Convert Nominal Attributes to Dummy Variables
Summary
How To Handle Missing Values In Machine Learning Data
Mark Missing Values
Remove Missing Data
Impute Missing Values
Summary
How to Perform Feature Selection With Machine Learning Data
Feature Selection in Weka
Correlation Based Feature Selection
Information Gain Based Feature Selection
Learner Based Feature Selection
Select Attributes in Weka
What Feature Selection Techniques To Use
Summary
How to Use Machine Learning Algorithms
Weka Machine Learning Algorithms
Which Algorithm To Use
Linear Machine Learning Algorithms
Nonlinear Machine Learning Algorithms
Ensemble Machine Learning Algorithms
Machine Learning Algorithm Configuration
Get More Information on Algorithms
Summary
How To Estimate The Performance of Machine Learning Algorithms
Model Evaluation Techniques
Which Test Option to Use
What About The Final Model
Performance Summary
Summary
How To Estimate A Baseline Performance For Your Models
Importance of Baseline Results
Zero Rule For Baseline Performance
Baseline Performance for Classification Problems
Summary
How To Use Top Classification Machine Learning Algorithms
Classification Algorithm Tour Overview
Logistic Regression
Naive Bayes
Decision Tree
k-Nearest Neighbors
Support Vector Machines
Summary
How To Use Top Regression Machine Learning Algorithms
Regression Algorithms Overview
Linear Regression
k-Nearest Neighbors
Decision Tree
Support Vector Regression
Multilayer Perceptron
Summary
How to Use Top Ensemble Machine Learning Algorithms
Ensemble Algorithms Overview
Bootstrap Aggregation
Random Forest
AdaBoost
Voting
Stacked Generalization
Summary
How To Compare the Performance of Machine Learning Algorithms
Best Machine Algorithm For A Problem
Compare Algorithm Performance in Weka
Design The Experiment
Run The Experiment
Review Experiment Results
Debugging Errors With Experiments
Summary
How to Tune the Parameters of Machine Learning Algorithms
Improve Performance By Tuning
Algorithm Tuning Experiment Overview
Design The Experiment
Run The Experiment
Review Experiment Results
Summary
How to Save Your Machine Learning Model and Make Predictions
Tutorial Overview
Finalize a Machine Learning Model
Save Finalized Model To File
Load a Finalized Model
Make Predictions on New Data
Summary
III Projects
How To Work Through a Multiclass Classification Project
Tutorial Overview
Load Dataset
Analyze the Dataset
Evaluate Algorithms
Finalize Model and Present Results
Summary
How To Work Through a Binary Classification Project
Tutorial Overview
Load the Dataset
Analyze the Dataset
Prepare Views of the Dataset
Evaluate Algorithms
Finalize Model and Present Results
Summary
How to Work Through a Regression Machine Learning Project
Tutorial Overview
Load the Dataset
Analyze the Dataset
Prepare Views of the Dataset
Evaluate Algorithms
Tune Algorithm Performance
Evaluate Ensemble Algorithms
Finalize Model and Present Results
Summary
IV Conclusions
How Far You Have Come
Getting More Help
Weka Offline Documentation
Weka Online Documentation
Stack Overflow
Summary