Business Intelligentization Using
Machine Learning and Rapidminer
Date: 20 Apr, 21 Apr 2023 | 9am-5.30pm | Online
Duration: 2 Days
Course Overview
Machine Learning (ML) operates on the basis of algorithms that enable computers to learn, mostly from data. ML algorithms attempt to be trained directly by data samples without relying vastly on a predetermined design and coding. ML systems can also adapt to the new situation and enhance their performance over the time. The course will introduce some of the algorithms in the field of ML, such as neural networks, deep learning, support vector machines, K nearest neighbours, and c-means clustering to name but a few. Rapidminer is a popular data mining and machine learning software platform that provides an integrated environment for realization of almost all known machine learning algorithms, and is accessible to the public as a free desktop application. Organisations can leverage on Rapidminer for many real-world ML applications including data preparation, results visualisation, model validation and optimisation.
This module is part of Professional Certificate in Applied Machine Learning.
Course Objectives
At the end of this course, participants will be able to:
- Understand a wide spectrum of ML algorithms, their parameters, and applications
- Know how to use Rapidminer to implement ML algorithms
- Understand how to develop machine learning models and how to evaluate them
- How to represent the machine learning results using Rapidminer visualization facilities
Who Should Attend
Machine Learning Developers, Python Developers
Prerequisites
Basic AI and Machine Learning knowledge
Course Convener
(Click their photos to view their short biographies)
Dr Amirhassan Monajemi
Dr Amirhassan Monajemi
Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications.
He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007.
Dr Monajemi has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.
Course Fees
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email soc-ace@nus.edu.sg
To register, click Register
Course Code:
TGS-2022011495
TGS-2022011576 (Synchronous e-learning)
For members of public and NUS Alumnus (without R&G Voucher), please follow the steps below:
Select Short Course / Modular Course -> Apply for Myself -> Browse Academic Modules / Short Courses-> Module/Course Category -> Short Courses -> Browse Courses-> Advanced Computing for Exe (Faculty/Department / Unit)
Please download the user guide for NUS Online Application Portal after you click ‘Apply for Myself’ if you need assistance.
Course Fee Breakdown
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older
