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

Related Courses

Knowledge Discovery in Databases (KDD)

  21 hours

Statistics with SPSS Predictive Analytics Software

  14 hours

Data Mining

  21 hours

From Data to Decision with Big Data and Predictive Analytics

  21 hours

Data Mining with R

  14 hours

Oracle SQL Intermediate - Data Extraction

  14 hours

Data Mining and Analysis

  28 hours

Introductory R for Biologists

  28 hours

Data Mining & Machine Learning with R

  14 hours

Data Visualization

  28 hours

Data Science for Big Data Analytics

  35 hours

Process Mining

  21 hours

Data Vault: Building a Scalable Data Warehouse

  28 hours

MonetDB

  28 hours

Foundation R

  7 hours