Machine Learning in Python

Date: 18 Jan, 19 Jan, 20 Jan 2023 | 9am-5.30pm |Classroom Learning

Duration: 3 Days

Course Overview

Machine learning is a branch of Artificial Intelligence which makes use of sample data known as training data to build a model for making predictions or decisions without being explicitly programmed to do so. It has been applied in many applications such as email filtering, text analysis, sales forecasting, digital marketing, computer vision, healthcare, etc. where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. In this course, participants will learn the fundamental concepts in Machine Learning. These include Supervised and Unsupervised Learning methods such as Linear Regression, Logistics Regression, Support Vector Machines, K-Nearest Neighbours, K-Means and Hierarchical Clustering. This course teaches how to use Python to create Machine Learning applications.

This module is part of Professional Certificate in Machine Learning Using Python

Course Objectives

This course aims to:

  • Provide participants with a strong foundation in advanced analytics methods.
  • Provide participants with the necessary knowledge to appreciate the proper application of advanced analytics methods in business applications.
  • Teach participants the use of Python for creating Machine Learning applications.

At the end of the course, participants will be able to:

  • Understand various machine learning methods for classifying and clustering data.
  • Use Python to implement Machine Learning methods in business applications.

Who Should Attend

Managers, Research Analysts, Software Engineers

Prerequisites

Polytechnic diploma and have knowledge in Python programming.

Course Convener

(Click their photos to view their short biographies)

ccccc Danny Poo

Assoc Prof Danny PooAssoc Prof Danny Poo

Course Fees

Singapore Citizens
39 years old or younger
40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

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-2022011025

Course Fee Breakdown

Singapore Citizens

Singapore Citizens

39 years old or younger

Singapore Citizen

40 years old or older
Singapore PRs
Enhanced Training Support for SMEs
International Participants

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