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5 students
Training Date : 19 – 20 October 2021                                                                       

Machine Learning for Business Intelligence (MLBI) (อบรมเชิงปฏิบัติการพร้อมได้รับประกาศนียบัตรในระดับสากล)

เนื่องจากสถานการณ์ COVID-19 จึงขอเลื่อนการอบรมออกไปก่อน

สอบถามข้อมูลเพิ่มเติมไดที่ คุณกชพรรณ 02-583-9992 ต่อ 1425

** บรรยายภาคภาษาอังกฤษโดยวิทยากรต่างชาติ **

สำคัญ!!! กรุณารอการยืนยันเปิดการอบรมจากเจ้าหน้าที่ก่อนการชำระค่าลงทะเบียน

Time : 12 Hour(s)
Days : 2 Day(s)
Duration : 09:00 – 16:00
Fee : 22,000 THB (Excluded Vat 7%)
Language : English
Instructor : Dr.Tarun Sukhani
Dr.Lim Meng Hui
Mr.Fares Hasan
Objectives : Course Overview

In this course, we introduce the field of machine learning and describe the well-known processes, algorithms, and tools for one to be a successful machine learning practitioner. This course will help to build skills in data acquisition and modeling, classification, and regression. In addition, one will also get to explore very important tasks such as model validation, optimization, scalability, and real-time streaming.

Course Objectives

– Introducing the basic concepts and practical applications of Machine Learning algorithms.
– Providing students with the capabilities to: (i) identify the long-term impact of machine learning to businesses; (ii) apply machine learning algorithms to their own real-world problems.

Who Should Attend : Prerequisite

Anyone working with Business Intelligence and Data Analysis.

Target Audience

Anyone who is keen to learn to learn more in-depth about Machine Learning and the real applications of Machine Learning for Business Intelligence today.


Students will be given a Certificate of Attendance after successfully completing the course.

Benefits : Students will be able to:

– Explain machine learning concepts & describe applications of well-known machine learning algorithms
– Apply machine learning techniques to a list of practical problems

Course Outline : Part I: The Machine Learning Workflow

1. What is machine learning?
1.1 How Machines Learn
1.2 Using Data to Make Decisions
1.3 The Machine Learning Workflow: from Data to Deployment
1.4 Boosting Model Performance with Advanced Techniques

2. Real-world data
2.1 Data collection
2.2 Pre-processing data for modeling
2.3 Using data visualization

3. Modeling and prediction
3.1 Basic machine learning modeling
3.2 Classification
3.3 Regression

4. Model evaluation and optimization
4.1 Model generalization: evaluating predictive accuracy for new data
4.2 Evaluation of classification models
4.3 Evaluation of regression models
4.4 Model Optimization through Parameter Tuning

5. Basic feature engineering
5.1 Why is Feature Engineering Useful?
5.2 Basic feature engineering process
5.3 Feature selection

Part II: Practical Applications

6. Example: NYC taxi data
6.1 Data visualization and preparation
6.2 Modeling

7. Advanced feature engineering
7.1 Advanced text features
7.2 Image features
7.3 Time-series features

8. Advanced Natural Language Processing (NLP) example: movie review sentiment
8.1 Exploring data and use case
8.2 Extracting basic NLP features and building the initial model
8.3 Advanced algorithms and model deployment considerations

9. Scaling machine-learning workflows
9.1 Before scaling up
9.2 Scaling Machine learning modeling pipelines
9.3 Scaling predictions

10. Example: digital display advertising
10.1 Digital Advertising
10.2 Digital Advertising Data
10.3 Feature Engineering and Modeling Strategy
10.4 Size and Shape of Data
10.5 Singular Value Decomposition
10.6 Resource Estimation and Optimization
10.7 Modeling
10.8 K-nearest neighbors
10.9 Random forests
10.10 Other Real Word Considerations

Payment Condition : 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



– 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.

Contact Person : For more information, contact our course coordinator on:

Name: Ms.Kotchaphan Aokdeelert

Tel: +66-2583-9992 Ext. 1425

Fax: +66-2583-2884

Email: kotchaphan.aokdeelert@nstda.or.th or 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. 

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Course Features

  • Lectures 0
  • Quizzes 0
  • Students 5
  • Assessments Yes