Machine Learning driven Data Science

Overview

Machine Learning uses techniques to deal with data in the most intelligent way - by developing algorithms - to derive actionable insights. With machine learning, you can glean useful patterns from the deep, focused troves of data specific to your chosen domain. Analysing that data provide insights that can drive successive new waves of efficiency and automation, reducing operational costs and potentially pinpointing new sources of revenue.

Interested candidates are required to complete the Python for Data, Ops and Things course.

Through the course, you will experience the 3 building blocks in machine learning:  

  1. Concepts and intuition: Participants learn and apply the concepts of machine learning using a methodology. You will learn to navigate smoothly through the data sciences and machine learning space by not only creating but also debugging your products with ease. You will then apply the same concepts covered in a real world scenario while in class.

     

  2. Architecture: Participants learn what is required to architect a data science platform / team and how to effectively design a machine learning driven data sciences product using a wide range of techniques that are taught and practised in class. This includes, but is not limited to, understanding different parts of a data science product. The main goal of this course is to provoke thinking, establish the context of learning with the objective of developing and enhancing your capabilities in establishing a machine learning product.

     

  3. Implementation: Finally, participants will develop a machine learning driven data science product. Using their accumulated capabilities, you are able to develop and complete a basic and complete product using machine learning driven data science. 

This course is part of the StackUp - Startup Tech Talent Development programme offered by NUS-ISS.

  • Machine Learning driven Data Science
  • Machine Learning driven Data Science

Enquiry

Please contact Ms. Sherlyn LIM at tel: 65165777 or email for more details.

Facts

Class Time: 9:00am - 5:00pm

Upcoming Classes

Class 116 Nov 2017 to 22 Dec 2017 (Full Time)

Duration: 25 days

When:
Nov:
16, 17, 20, 21, 22, 23, 24, 28, 29, 30
Dec:
01, 04, 05, 06, 07, 08, 11, 13, 14, 15, 18, 19, 20, 21, 22
Time:
09:00am to 05:00pm

Class 219 Feb 2018 to 27 Mar 2018 (Full Time)

Duration: 25 days

When:
Feb:
19, 20, 21, 22, 23, 26, 27, 28
Mar:
01, 02, 05, 06, 07, 08, 09, 12, 13, 14, 15, 16, 19, 20, 21, 22, 23, 26
Time:
9:00am - 5:00pm

Class 307 May 2018 to 12 Jun 2018 (Full Time)

Duration: 25 days

Time:
09:00am to 05:00pm

Class 413 Aug 2018 to 18 Sep 2018 (Full Time)

Duration: 25 days

Time:
09:00am to 05:00pm

Class 507 Nov 2018 to 12 Dec 2018 (Full Time)

Duration: 25 days

Time:
09:00am to 05:00pm

Class 625 Feb 2019 to 01 Apr 2019 (Full Time)

Duration: 25 days

Time:
09:00am to 05:00pm

Course Details

  • Key Takeaways
  • Who Should Attend
  • What Will Be Covered
  • Fees & Funding
  • Certificate
  • Preparing for Your Course
  • Attain intuition and understanding of what is intelligence and learning
    • Ability to design a machine learning problem
    • Ability to design data science experiments
  • Attain intuition of what can be gained through data
    • Ability to determine the difference between good and bad data
    • Ability to store intelligence in a model
    • Ability to collect data through various data sampling methods
    • Ability to implement machine learning techniques and algorithms
  • Attain intuition and understanding of when and where each technique is used
    • Ability to improve a machine learning model to reach a target goal
    • Ability to validate a solution and detect if a machine learning model is guessing
    • Ability to design a data science and machine learning architecture
    • Ability to create a basic data science and machine learning enterprise-level product
  • Any professional from domains (e.g. healthcare, finance, manufacturing) that need to manage and work with data
  • Data engineers, researchers, healthcare professionals and more

Pre-requisite

Completion of Python for Data, Ops and Things course.

What to Bring

Please bring along your laptop during the training.
Specs: 8 GB RAM minimum
16 GB RAM recommended
64 GB storage 1280x1024 minimum screen resolution
Windows 10 or Later/Mac OSX 10.10 or Later (64-Bit OS Required)

 

Introduction & Problem Design

  • What is artificial intelligence and machine learning?
  • Discovering the limits of data science and machine learning through use cases
  • Machine learning problem formulation from project ideation
  • Use case: Online store model project definition (e-commerce use case project)

Data Collection, Sampling, and Cleaning

  • Concept of data, information, and learning
  • Understand concepts of data cleaning and manipulation
  • Use case: Collecting trend data from purchases
  • Use case: Identifying timing groups for customer visits (regional/age/gender product placement, server load)

Training and Evaluation

  • Gain conceptual understanding of training strategies in ML
  • Studying evaluation strategies in machine learning problems
  • Train a model using a preprocessed dataset and a black box algorithm
  • Evaluate model results, then identify and fix bugs in the dataset
  • Identify and analyse the performance of a learned model
  • Use case: Product classification

Classification

  • Understand and implement algorithms to train probabilistic models
  • Study and implement Bayesian algorithms for prediction
  • Implement machine learning algorithms
  • Use case: Recommendation systems

Decision Rules

  • Understand and implement decision rules based machine learning algorithms
  • Train a model using an algorithm to predict behaviour (customers in e-commerce domain)
  • Model customer behaviour patterns in the form of decision rules
  • Train a model using multiple algorithms
  • Use case: Generate purchase patterns

Regression

  • Understand and implement regression analysis algorithms
  • Study staging problems to predict future behaviour based on the current state
  • Use case: Converting and non-converting customers (lead conversion) regression

Text & Image Classification

  • Understand and implement support vector machines, neural networks, and convolutional neural networks
  • Introduction to natural language processing and deep learning
  • Introduction and experimentation with Tensorflow and Tensorboard
  • Use case: Good feedback and back feedback based on user comments
  • Use case: Generating product descriptions using images

Capstone Project

  • Using a subset of the techniques covered in class, create a solution to a basic enterprise-level problem/project
  • Guest lectures for natural language processing, medical computing, and reinforcement learning

Format

Lectures, discussions and exercises

Self-sponsored

International Participants

S'poreans and PRs 
(aged 21 and above)

SkillsFuture Mid-Career Enhanced Subsidy1 
(S’poreans aged 40 and above)

Workfare Training Support2
(S’poreans aged 35 and above, and earn ≤ $2,000 per month)

Full course fee

S$10000

S$10000

S$10000

S$10000

SSG grant

-

(S$7000)

(S$7000)

(S$7000)

Nett course fee

S$10000

S$3000

S$3000

S$3000

7% GST on nett course fee

S$700

S$210

S$210

S$210

Total nett course fee payable, including GST

S$10700

S$3210

S$3210

S$3210

Less additional funding if eligible under various schemes

-

-

(S$2000)

(S$2500)

Total nett course fee payable, including GST, after additional funding from the various funding schemes

S$10700

S$3210

S$1210

S$710


Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees, apart from government subsidies. For more information, click here.

Company-sponsored

International Participants

S'poreans and PRs 
(aged 21 and above)

SkillsFuture Mid-Career Enhanced Subsidy1 
(S’poreans aged 40 and above)

Workfare Training Support2
(S’poreans aged 35 and above, and earn ≤ $2,000 per month)

Enhanced Training Support for SMEs3

Notes

Full course fee

S$10000

S$10000

S$10000

S$10000

S$10000

SSG grant

-

(S$7000)

(S$7000)

(S$7000)

(S$7000)

Nett course fee

S$10000

S$3000

S$3000

S$3000

S$3000

7% GST on nett course fee

S$700

S$210

S$210

S$210

S$210

Total nett course fee payable, including GST

S$10700

S$3210

S$3210

S$3210

S$3210

Fee payable to NUS-ISS

Less additional funding if eligible under various schemes#
(company needs to submit training grant and claim via Skillsconnect)

-

-

(S$2000)

(S$2500)

(S$2000)

Total nett course fee payable, including GST, after additional funding from the various funding schemes

-

-

S$1210

S$710

S$1210

Actual financial outlay by company

Various Funding Schemes

1Mid-Career Enhanced Subsidy

  • Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.


2
Workfare Training Support (WTS)

  • Singaporeans aged 35 and above (13 years and above for Persons With Disabilities) and earn not more than $2,000 per month, may enjoy subsidies up to 95% of the course fees.


3
Enhanced Training Support for SMEs (ETSS)

  • SME-sponsored employees (Singaporean Citizens and PRs) may enjoy subsidies up to 90% of the course fees. For more details, click on Enhanced Training Support for SMEs.


Course attendee is eligible for only one funding scheme.

#For company-sponsored participants, companies would need to pay upfront 30% of the course fee to NUS-ISS and submit a training grant application for the remaining eligible subsidies, and subsequently a claim in Skillsconnect. For details, please refer to Skillsconnect guide 4.1 & 5.1.

This course is aligned to the National Infocomm Competency Framework (NICF) and accredited by SSG. Absentee payroll and up to 70% SSG funding of the course fee is available for eligible participants (Singapore Citizens and Permanent Residents). Absentee payroll subsidy is available for eligible companies and companies on a short work week system will receive the absentee payroll subsidy based on their employees' current income. Please visit www.ssg.gov.sg for full details.


Certificate of Completion

The ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course.

Assessment

Participants will be assessed based on their aptitude, attitude and the quality of deliverables produced.

Participants may need to attend additional coaching sessions and re-assessments if they do not pass. ISS reserves the right not to disclose any information on the course assessment process.

NUS-ISS Course Registration Terms and Conditions

Find out more.

NUS-ISS and Learner’s Commitment and Responsibilities

Find out more.

WIFI Access

WIFI access will be made available. User ID and password will be provided in the participant’s kit.

Venue

NUS-ISS Stackup Studio
JTC LaunchPad @ One North
Block 79 Ayer Rajah Crescent #02-09
Singapore 139957

In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to isstraining@nus.edu.sg or call 6516 2093 if you have any enquiry or feedback.

Course Resources

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