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Course Outline
Introduction
- Overview of Weka
- Understanding the data mining process
Getting Started
- Installing and configuring Weka
- Understanding the Weka UI
- Setting up the environment and project
- Exploring the Weka workbench
- Loading and Exploring the dataset
Implementing Regression Models
- Understanding the different regression models
- Processing and saving processed data
- Evaluating a model using cross-validation
- Serializing and visualizing a decision tree model
Implementing Classification Models
- Understanding feature selection and data processing
- Building and evaluating classification models
- Building and visualizing a decision tree model
- Encoding text data in numeric form
- Performing classification on text data
Implementing Clustering Models
- Understanding K-means clustering
- Normalizing and visualizing data
- Performing K-means clustering
- Performing hierarchical clustering
- Performing EM clustering
Deploying a Weka Model
Troubleshooting
Summary and Next Steps
Requirements
- Basic knowledge of data mining process and techniques
Audience
- Data Analysts
- Data Scientists
14 Hours
Testimonials (5)
how the trainor shows his knowledge in the subject he's teachign
john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course - Data Vault: Building a Scalable Data Warehouse
Prepared material. Full professionalism. Very good contact with the trainer. Full engagement and openness to changing the planned training format (very valuable open discussions on the topics we prepared)
Kamil Trebacz - Bank Gospodarstwa Krajowego
Course - Pentaho Data Integration (PDI) - moduł do przetwarzania danych ETL (poziom zaawansowany)
Machine Translated
Open discussion with trainer
Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.
Course - Process Mining
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
Course - Foundation R
The example and training material were sufficient and made it easy to understand what you are doing.
