Home > Courses > Machine Learning Systems for Engineers - Part of Modular Certificate in Machine Learning and Data Visualization (Elective)(DEE1)

Machine Learning Systems for Engineers - Part of Modular Certificate in Machine Learning and Data Visualization (Elective)(DEE1)

Name: Machine Learning Systems for Engineers - Part of Modular Certificate in Machine Learning and Data Visualization (Elective)(DEE1)
Price: 1866.00 SGD

Training Provider: Ngee Ann Polytechnic

Course Reference: TGS-2024049628

S$1,866

About This Course

Machine Learning Systems for Engineers - Part of Modular Certificate in Machine Learning and Data Visualization (Elective)(DEE1) is a 60hrs module.

What You'll Learn

This module will help students to apply AI techniques to their fields in order to get accurate solutions. This module covers the basics of artificial intelligence, machine learning and deep learning. It deals with different types of learning techniques lke: supervised, unsupervised and reinforced. The concept of features, data handling, training and testing will be explained. It focuses more on the theory, design and applications of artificial neural networks (ANN)

Entry Requirements

The following GCE ‘O’ Level grades and with at least 1 year of relevant work experience:
– English Language (Grade 1-7)
– Mathematics (Grade 1-6)
– Science (Grade 1-6)

OR NITEC with GPA >= 3.5
OR NITEC with GPA >= 3.0 and with at least 1 year of relevant work experience

OR Higher NITEC with GPA >= 2.0
OR Higher NITEC with GPA >= 1.5 and with at least 1 year of relevant work experience

OR NITEC in Technology or Services with GPA >= 3.5 and with at least 1 year of relevant working experience

OR Higher NITEC in Technology or Services with GPA >=2.0 and with at least 1 year of relevant working experience

OR relevant Advanced WSQ Certificate (or higher) and Level 6 WSQ Workplace Literacy SOA and Workplace Numeracy SOA and with at least 1 year of relevant working experience

OR applicants with at least 2 years of relevant working experience may apply for the course

Recognition of Prior Learning (RPL)
Applicants who do not meet the entry requirements may be consider