Introduction to Machine Learning
Still no participant
Still no reviews
INTRODUCTIONMachine learning has been around for decades but was only used for 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 such as TensorFlow and Scikit-Learn. The course is divided into two parts: 1) Fundamentals of Machine Learning and 2) Building and implementing machine learning models with TensorFlow. The first part introduces types of machine learning systems, the typical workflow of a machine learning project, and going through an example project using some datasets. The second part covers basics of TensorFlow and implementing Neural Network models using TensorFlow in distributed computing environment. At the end of the course, you will have the ability to utilize TensorFlow to solve problems using Neural Network or other machine learning models.
OBJECTIVESUpon completion of this course, participants will be able to:
- Understand the fundamentals of machine learning.
- Understand the basics of TensorFlow.
- Able to use TensorFlow to solve complex problems with machine learning models.
COURSE CONTENTSIntroduction to Machine Learning
- Typical workflow of Machine Learning project
- Basics of Python and TensorFlow
- Getting started with TensorFlow
- Introduction to Neural Network
- Training Deep Neural Network
- Running TensorFlow in distributed environment
TRAINERSDr 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 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. Download the Course Brochure: Introduction to Machine Learning
REGISTRATION IS NOW OPENTo register, download the CAPE Professional Short Course Registration Form and email the completed form to firstname.lastname@example.org
Our Main Teachers
INTRODUCTION Machine learning has been around for decades but was only used for 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 […]
Price : 600 (Discount available for students, early bird & group registration) MYR MYR
Max Availability : 25
Location : MS Teams