Problem Solving with Machine Learning
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INTRODUCTIONMachine learning has been around for decades, but its usage was limited to specialized applications such as Optical Character Recognition (OCR) due to constraints in computing resources. With advancements in computing and communication technology, powerful computing resources are becoming more affordable, and it is becoming possible to implement and use machine learning to solve complex problems in various application domains effectively. Whether you are in healthcare, banking, or manufacturing domains, machine learning may suit your needs. This course assumes you know close to nothing about Machine Learning. You will learn the concepts and tools needed to implement programs that learn from data by using production-ready Python frameworks. The course comprises of two parts: 1) Fundamentals of Machine Learning and 2) Building and implementing machine learning models with Jupyter Notebook. The first part introduces the types of machine learning techniques, the typical workflow of a machine learning project, and going through an example project using some datasets. The second part covers the basics of Jupyter Notebook, implementation of the Machine Learning Models in both non-distributed and distributed computing environment. At the end of the course, you will have the ability to utilize machine learning models to solve some real problems.
OBJECTIVESUpon completion of this course, participants will be able to: 1.Understand the fundamentals of machine learning. 2.Able to use Python language and the Machine Learning tools. 3.Able to solve complex problems with machine learning models.
- Overview of Machine Learning
- Typical Workflow of Machine Learning Project
- Data Analysis methods
- Getting Started with Jupyter Notebook
- Implementing Machine Learning models
- Running Machine Learning models in Distributed Environment
Assoc. Prof. Dr Mohd Soperi bin Mohd ZahidDr Mohd Soperi is an Associate Professor at Computer and Information Sciences Department, Universiti Teknologi PETRONAS. He obtained his PhD in Computer Science from University of Wisconsin – Milwaukee, USA in 2009, M Sc in Computer Integrated Manufacturing from Rochester Institute of Technology, USA in 1998 and B Sc in Computer Science from New Mexico State University, USA in 1988. His research interests include Internet and Delay Tolerant routing protocols, Software Defined Networks failure recovery, and Cardiovascular diseases detection using Machine Learning. He has published several journals and conference papers in these areas.
Dr. Khaleel HusainDr Khaleel Husain completed his PhD in Universiti Teknologi PETRONAS and Master’s degree in Digital Communication and Networking under the Department of Electronics and Communication Engineering from Visvesvaraya Technological University (VTU), India in 2014. Prior to his Master’s, he has completed his Bachelor’s degree in Electronics and Communication Engineering from VTU in 2012. His research interest lies in the field of Wireless Communications (Routing protocols, receiver-based mechanism, Software Defined Networks and reliable data transmissions), and Machine Learning algorithms.
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INTRODUCTION Machine learning has been around for decades, but its usage was limited to specialized applications such as Optical Character Recognition (OCR) due to constraints in computing resources. With advancements in computing and communication technology, powerful computing resources are […]
Price : 550 (Discount available for students, early bird & group registration) MYR
Max Availability : 25
Location : MS Teams