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

Weka is a top machine learning platform that provides an easy-to-use graphical interface and state-of-the-art algorithms. In this mega Ebook is written in the friendly Machine Learning Mastery style, learn exactly how to get started with applied machine learning using the Weka platform. 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