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 113 Aug 2018 to 18 Sep 2018 (Full Time)

Duration: 25 days

When:
Aug:
13, 14, 15, 16, 17, 20, 21, 23, 24, 27, 28, 29, 30, 31
Sep:
03, 04, 05, 06, 07, 11, 12, 13, 14, 17, 18
Time:
9:00am - 5:00pm

Class 207 Nov 2018 to 12 Dec 2018 (Full Time)

Duration: 25 days

When:
Nov:
07, 08, 09, 12, 13, 14, 15, 16, 19, 20, 21, 22, 23, 26, 27, 28, 29, 30
Dec:
03, 04, 05, 06, 07, 10, 11, 12
Time:
9:00am - 5:00pm

Course Details

  • Key Takeaways
  • Who Should Attend
  • What Will Be Covered
  • Fees & Funding
  • Certificate
  • Preparing for Your Course

By the end of the course, attendees should be able to:

  • Describe what Machine Learning is, its applications, and limitations
  • Apply classical machine learning techniques using Python libraries for classification, regression, and clustering problems
  • Apply the machine learning workflow (data preparation, feature engineering, training and validation) for supervised and unsupervised learning problems
  • Describe well-known and advanced deep learning models and applications
  • Apply deep learning techniques using Python libraries for object detection, text processing, time series forecasting, and speech processing
  • Apply the machine learning workflow (data preparation, feature engineering, training and validation) for deep learning problems in accelerated (GPU) training environments
  • Apply course learning in iteratively designing, validating, and improving a data experiment for a domain-specific problem or class of problems.
  • Compare the effectiveness of different techniques (including not using machine learning) on solving a domain-specific problem or class of problems.
     
     
  • 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)

 

Getting Started with Machine Learning

  • What is machine learning
  • NumPy, Pandas, and Scikit-learn
  • Machine learning workflow
  • Building machine learning models
  • Validating machine learning models
  • Training environments: cloud, CPU, GPU

Introduction to Basic Machine Learning Algorithms

  • Linear regression
  • Logistic regression
  • Naïve Bayes
  • Support Vector Machines
  • Clustering
  • K-nearest Neighbour algorithm

Introduction to Text and Image Processing using Deep Learning

  • Convolutional Neural Networks for Object Detection
  • Keras with Tensorflow
  • Transfer Learning
  • Introduction to Generative Adversarial Networks
  • Text Processing and Word Embeddings
  • NLTK and gensim
  • Recurrent Neural Networks with Long Short Term Memory

Introduction to Time Series, Speech Processing, and Advanced Deep Learning

  • Time series forecasting using Auto-regressive, Moving average, and Differentiative methods
  • Time series forecasting using Long Short Term Memory
  • Introduction to Speech Recognition
  • Introduction to Reinforcement Learning
  • Introduction to Capsule Networks

Best Practices

  • Data Engineering Best Practices
  • Feature Engineering Best Practices
  • Model Validation Best Practices
  • Debugging techniques
  • Model deployment

Capstone Project

  •  Using a subset of the techniques covered in class, create a solution to a basic enterprise-level problem/project 

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 will be capped at 156 hours and 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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