ADVANCED ANALYTICS AND MACHINE LEARNING USING PYTHON (SF)

ADVANCED ANALYTICS AND MACHINE LEARNING USING PYTHON (SF)

Funded

Course Duration

16.0 hr(s)

Mode of Assessment

Written Assessment, Practical Assessment

Who Should Attend

This particular course should be attended by those who want to learn machine learning using Python. Applicable to students, working professionals and PMETs.

What's In It for Me

  • Understand the behind the scenes of machine learning
  • Learn various supervised and unsupervised machine learning algorithms
  • Understand how to choose the right models for the right business case
  • Learn how to optimize machine learning models

Course Overview

The Advanced Analytics and Machine Learning using Python course is designed to equip students with advanced skills in data analysis, statistics, and machine learning using the Python programming language. The course is suitable for individuals who have a basic understanding of Python programming and wish to further their knowledge in the area of advanced analytics and machine learning.

Throughout the course, students will explore various machine learning algorithms, such as decision trees, support vector machines, and others, and learn how to implement them in Python using popular libraries. In addition to theoretical concepts, the course emphasizes on the practical applications of advanced analytics and machine learning in real-world scenarios. Participants will work on various case studies, including data preprocessing, feature engineering, model selection, and evaluation.

Upon completion of the course, students will be able to confidently apply advanced analytics and machine learning techniques to a wide range of data-driven problems.

Course Schedule

Next available schedule

See All Schedules

Course Objectives

  • Evaluate the business problems in the financial industry today and the data associated with them to develop predictive analytics applications
  • Understand and use the various algorithms and tools to work with the data and test hypotheses
  • Construct analytics models/results as part of solutions to address business problems and derive patterns and solutions
  • Learn to code machine learning algorithms and build models using Python
  • Evaluate the performance of analytics models and learn how to optimize models
  • Learn the various statistical models, machine learning algorithms, and understand how to use them in various business scenarios
  • Evaluate the importance of data and features which are used to solve business problems
  • Analyze the results or outputs of analytics models
  • Understand and evaluate possible big data applications in conjunction with machine learning

Pre-requisites

  • This course requires a basic understanding of Python. Participants who do not have the basic knowledge are encouraged to take up Fundamentals of Python Programming prior to this course.
  • Hardware & Software
    • This course will be conducted as a Virtual Live Class (VLC) via Zoom platform.
    • Participants must own a Zoom account and have a laptop or a desktop with “Zoom Client for Meetings” installed. Download from zoom.us/download.

System Requirement

Must-have:
Please ensure that your computer or laptop meets the following requirements.

  • Operating system: Windows 10 or MacOS (64 bit or above)
  • Processor/CPU: 1.8 GHz, 2-core Intel Core i3 or higher
  • Minimum 20 GB hard disk space
  • Minimum 8 GB RAM
  • Webcam (camera must be turned on for the duration of the class)
  • Microphone
  • Internet Connection: Wired or Wireless broadband
  • Latest version of Zoom software to be installed on computer or laptop prior to the class.

Good-to-have:

  • Wired internet connection
    Wired internet will provide you with stable and reliable connection.
  • Dual monitors
    Using a dual monitor setup will undoubtedly improve your training experience, enabling you to simultaneously participate in hands-on exercises and maintain engagement with your instructor.

Not recommended:
Using tablets are not recommended due to their smaller screen size, which could cause eye strain and discomfort over the course of the program's duration.

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

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)

● Implementing clustering with real use cases

Module 3: Supervised Machine Learning

Supervised machine learning is one of the most popular techniques 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

            ○ Linear Regression

○ Logistic Regression

○ Support Vector Machines

○ Decision Trees

● Implementing the above algorithms with real use cases

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 optimized over time. This module aims to do just that by covering the techniques aiding model selection and evaluation and optimization.

● Understanding model selection and evaluation methods

● Optimize machine learning models

Certificate Obtained and Conferred by

  • Certificate of Achievement from NTUC LearningHub will be issued to participants who have met at least 75% attendance and passed the prescribed assessment(s).
  • Upon meeting at least 75% attendance and passing the assessment(s), Statement of Attainment (SOAs) will be issued by SkillsFuture Singapore (SSG) to certify that the participant has achieved the following Competency Standard(s):
    • Computational Modelling (ICT-DIT-3021-1.1)
  • Certificate of completion from NTUC LearningHub

Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from NTUC LearningHub.

Additional Details

Medium of Instruction: English
Trainer to trainee Ratio: 1:20
Mode of Delivery: Virtual Live Class (VLC) via Zoom or Physical class

Price

Course Fee and Government Subsidies

  

Individual Sponsored 

Company Sponsored 

 

Non-SME 

SME 

Before GST 

After GST 

Before GST 

After GST 

Before GST 

After GST 

Full Course Fee
(For Foreigners and those not eligible for subsidies)

$1,250.00

$1,362.50

$1,250.00

$1,362.50

$1,250.00

$1,362.50

For Singapore Citizens aged 39 years and below
and
For all Singapore Permanent Residents
(The minimum age for individual sponsored trainees is 21 years)

$375.00

$408.75

$375.00

$408.75

$125.00

$158.75

For Singapore Citizens aged 40 years and above

$125.00

$158.75

$125.00

$158.75

$125.00

$158.75


Funding Eligibility Criteria:

Individual Sponsored Trainee

Company Sponsored Trainee
  • Singapore Citizens or Singapore Permanent Residents

  • From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses

  • Trainee must pass all prescribed tests / assessments, and attain 100% competency
  • 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

  • From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses

  • Trainee must pass all prescribed tests / assessments, and attain 100% competency
  • NTUC LearningHub reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria


Remarks:

Individual Sponsored Trainee

Company Sponsored Trainee

SkillsFuture Credit:

  • Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.

UTAP:

  • This course is eligible for Union Training Assistance Programme (UTAP).
  • NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP.

PSEA:

  • To check for Post-Secondary Education Account (PSEA) eligibility for this course, visit:
    (a)     SkillsFuture (TGS-2023035768) for Virtual Learning Class (VLC)
    (b)     SkillsFuture (TGS-2023035767) for Face-to-Face class
  • Scroll down to “Keyword Tags” to verify for PSEA eligibility. 
  • If there is “PSEA” under keyword tags, the course is eligible for PSEA.  
  • And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA. 
  • Not all courses are eligible for PSEA funding.

Absentee Payroll (AP) Funding:

  • $4.50 per hour, capped at $100,000 per enterprise per calendar year.
  • AP funding will be computed based on the actual number of training hours attended by the trainee.
  • Note: Courses / Modules under Professional Conversion Programme (PCP) will not be eligible for AP funding claim.


Terms & Conditions apply. NTUC LearningHub reserves 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.

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