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Machine Learning and Advanced Analytics Using Python is a Program

Machine Learning and Advanced Analytics Using Python


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Full program description

Machine Learning and Advanced Analytics Using Python

Course Overview

Programme Code: P200511TDG

Understanding and analysing data is one of the key skills required in the industry today. This course is completely focused on the various aspects of data analytics using Python. Participants will be taught to use and taken through the key libraries for data ingestion and manipulation, exploratory data analysis, model building and data visualization as well as the basic statistics knowledge required to understand the concepts in the latter courses.

Who Should Attend

This particular course should be attended by those who want to learn data analytics using Python should attend this intermediate course. Applicable to students, working professionals and PMETs.

Course Duration

2 Days / 16 Hours (including 2 hours of assessment)

Course Outline

Module 1: Introduction to Machine Learning with scikit-learn

The objective is to understand the basics of machine learning and what it means. The module also introduces the basic concepts of supervised and unsupervised machine learning and introduces a very important library used for machine learning on Python scikit-learn.


  • Introducing the machine learning flow and concepts
  • Functions within scikit-learn
  • Introduction to supervised and unsupervised machine learning

Key Takeaways:

  • Understand the basic concepts of scikit-learn
  • Understand and learn the nuances of machine learning and the various learning types

Module 2: Unsupervised Machine Learning

This module aims to equip participants with the fundamentals of unsupervised machine learning using a very popular python library called scikit-learn. Unsupervised learning is very important across various business cases today, right from customer segmentation to property analysis.


  • Understanding unsupervised ML algorithms
  • Introduction to clustering (k-means, SOM)
  • Implementing clustering with real use cases

Key Takeaways:

  • Learn when to apply unsupervised learning algorithms
  • Understand the nuances of how unsupervised machine learning algorithms work

Module 3: Supervised Machine Learning

Supervised machine learning is one of the most popular technique in machine learning today. This module will stress on some of the most popular algorithms in regression and classification and equip participants with an understanding of how the algorithms work and where they can be used.


  • Introduction to various supervised learning algorithms
  • Understanding feature engineering and feature sets
  • Understanding and implementing

- Logistic Regression

- Support Vector Machines

- Decision Trees

- Bayesian Networks

  • Implementing the above algorithms with real use cases

Key Takeaways:

  • Learn how to apply supervised learning algorithms to business cases
  • Learn how to code supervised learning algorithms using Python
  • Learn how to test and validate machine learning models

Module 4: Evaluating machine learning models

One of the key steps in the data science lifecycle is to evaluate machine learning models to make sure the right one is selected for use in the business. Also, these models need to be trained and optimised over time. This module aims to do just that by covering the techniques aiding model selection and evaluation and optimisation.


  • Understanding model selection and evaluation methods
  • Optimise machine learning models

Key Takeaways:

  • Understand the keys concepts of model evaluation and performance metrics involved to avoid unintended outcomes
  • Learn to optimise machine learning models using various techniques

Mode of Assessment

Written and Practical

Certification Obtained and Conferred by

Awarded NTUC LearningHub Certificate of Completion


This course requires a basic understanding of Python such as its syntax and some Python libraries such as Pandas, Numpy and Matplotlib. Participants who do not have the basic knowledge are encouraged to take up Basics of Python prior to this course.

Hardware and Software Requirements

Participants attending Virtual Live Class are to ensure that their computer meet the requirements of this course.

  • Hardware:

  1. Web Camera
  2. Microphone

  • Software

  1. Support Windows and Mac
  2. Web Browser (Google Chrome is recommended)
  3. Anaconda Python 3.7 and above (
  4. Recommend Internet Speed 100Mbps (

Medium of Instruction & Trainer

Medium of Instruction: English

Trainer: Trainee ratio is 1: 20


Course Fee

Full Course Fee


Full Course Fee with GST


Nett amount after Funding

Singapore Citizens & Permanent Resident**






$1,070.00 *

* includes 7% GST

** 95% course fee funding under IBF Funding Scheme



  • Singapore Citizens or Singapore Permanent Residents physically based in Singapore
  • Trainee has to complete 100% attendance and pass all relevant assessments and examinations
  • NTUC LearningHub reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria


  • Singapore Citizens or Singapore Permanent Residents physically based in Singapore
  • Company must be Financial Institutions regulated by MAS (licensed or exempted from licensing), or involved in supporting financial sector activities. FinTech firms must be certified by Singapore FinTech Association (SFA)
  • Trainee has to complete 100% attendance and pass all relevant assessments and examinations
  • Eligible company-sponsored trainees will be able to clock CPD hours upon successful course completion

Funding Incentives:

1. Institute of Banking and Finance (IBF) Enhanced Funding

Singapore Citizens and permanent residents of all ages are entitled to receive enhance course fee subsidies of 95%. This funding is open to all individuals with no limitation to industry.

2. Training Allowance Grant

Self-sponsored individuals who successfully completed IBF accredited course will receive $10 per training and assessment hour. Training grant will be reimbursed to trainees PAYNOW account after course completion.

Sponsoring companies will receive $15 per training and assessment hour upon participants successful completion and passing the assessment.

3. SkillsFuture Credit

Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees, the credits may be used on top of existing course fee funding. This is only applicable to self-sponsored participants.

4. Union Training Assistance Programme (UTAP)

NTUC members can enjoy 50% unfunded course fee support for up to $250 each year. You must be a union member throughout the course duration and at the time of claim. You must achieve a minimum of 75% attendance for each application and have sat for all prescribed examinations. This is only applicable to self-sponsored participants. 

Terms and conditions apply. NTUC LearningHub reserve the right to make changes or improvements to any of the products described in this document without prior notice.

Prices are subject to other LHUB miscellaneous fees.