In this course provides students with the knowledge and skills to learn Java programming for Machine learning and Statistical learning with Weka library. The course focuses on teaching individuals how to apply Machine learning and Statistical learning in Weka, creating datasets, generating model, training/testing model and evaluating model.

This course is designed for an IT professional or developer who wants to get started in the field of Machine learning. Anyone with prior experience with java, data mining or statistical learning world will very quickly feel at ease with WEKA. To get the most out of the course, you should be somewhat familiar with Java. Prior exposure to any of these concepts will be helpful, but not required.

Day1:  Module 1: Machine Learning and WEKA Basics
                        • What is Machine learning and Weka?
                        • Core Algorithms types
                        • Rule Systems

          Module 2: Classification
                       • What is classification and classes?
                       • Classifying data in Weka
                       • Which algorithms will work for classification
                       • Running classification in Weka

         Module 3: Regression
                       • What is regression?
                       • Which algorithms will work for regression
                       • Running regression in Weka

Day2:  Module 4: Datasets for Weka
                       • Creating ARFF files
                       • Data types
                       • Class enumeration
          Module 5: Features and feature types
                       • What are features?
                       • Feature selection and Feature engineering
                       • Filtering algorithms based on feature-type in Weka

         Module 6: Model Evaluation
                      • Interpreting and Refining Results
                      • Class Balancing

Day3:  Module 7: Machine learning with Java and Weka
                        • Using Weka.jar
                        • Java/Weka classification project

          Module 8: Use case example: Naïve Bayes in Weka and Java
                        • Creating a model
                        • Importing data
                        • Analyzing data

         Module 9: Use case example: Neural Network in Weka and Java
                       • Creating a model
                       • Importing data
                       • Analyzing data

Payment can be made by:

    1. Cash or Credit Card or Bank Cheque payable to “Software Park Thailand #2” (a post-dated cheque is not accepted) on the first day of the service or within the last day of the service.
     2. Account transfer and send the proof of the payment (the deposit slip) via fax or email to fax no. 02-583-2884 or email ttd@swpark.or.th

        2.1 Siam Commercial Bank, Chaengwattana Branch
             Saving Account Number: 324-2-56262-0
             Account Name: Software Park Thailand#2

        2.2 Krungsri Bank, Chaengwattana (Software Park) Branch
             Saving Account Number: 329-1-34850-3
             Account Name: Software Park Thailand#2

Notes:
- Withholding tax (3%) is exempt.
- Should you need to withdraw, you must send the notice of the withdrawal in writing no later than 7 working days before the commencement date. The cancellation less than 7 days will be subject to a fine of 40% of the fee.
- Software Park Thailand reserves the rights to cancel courses due to unforeseen circumstances.

For more information, contact our course coordinator on:

Ms. Patsorn Pornthip

Tel: +66-2583-9992 Ext. 1422

Fax: +66-2583-2884

Email:patsorn@swpark.or.th, ttd@swpark.or.th

You are encouraged to use the course schedule as a guide to plan your training. The schedule is accessible at www.swpark.or.th for more information.