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Bundled Programme - Artificial Intelligence (SGUS)

6 Months Full time Online Synchronous Sessions by the Instructor & Mentor Professional Diploma in Artificial Intelligence(Bundled Programme - Artificial Intelligence)

What do I Get?

Acquire Artificial Intelligence Skills

Learn Python Machine Learning programming, statistical modelling, Azure Machine Learning models in JupyterNotebook, Deep Learning and Natural Language processing(NLP) to define the next generation of Artificial Intelligent solutions and making inferences from knowledge . 

Online Synchronous Sessions

We deliver online synchronous sessions by the Instructor & Mentor through a combination of instructor-led flipped classes and personalised mentoring with industry practitioners for the assignments and projects to greatly increase your efficiency and effectiveness in acquiring knowledge and skills.

Job Placement Assistance

We will assist you in getting hired with more than 50 hiring employers working with us.

SGUS Funding

Eligible Singapore Citizens / Permanent Residents can receive funding* under SGUS scheme.   

*Terms and conditions apply. Refer the price column

Audience and Certificates

Target Audience

  • PMETs who wants to acquire skills to develop intelligent applications to assume AI Developer job role

Prerequisite

Academic Level: At least 1 GCE 'A'/'H2' Level pass or equivalent (General)

 

Graduation Requirements

  • Minimum attendance of 75% for all Sessions in each of the modules of the qualification
  • Should be assessed Competent (C) in each of the modules of the qualification

Certificate(s)

  • Professional Diploma in Artificial Intelligence by Lithan Academy

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4002-1.1 Applications Development

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4005-1.1 - Data Engineering

  • Statement of Attainment by SSG, Singapore: ICT-SNA-4011-1.1 - Emerging Technology Synthesis

  • Statement of Attainment by SSG, Singapore: ICT-DIT-4001-1.1 - Analytics and Computational Modelling

  • Statement of Attainment by SSG, Singapore: ICT-PMT-4002-1.1 Programme Management

  • Statement of Attainment by SSG, Singapore: ICT-DIT-3006-1.1 - Data Visualisation

  • Statement of Attainment by SSG, Singapore: ICT-DES-4005-1.1 Software Design

  • Statement of Attainment by SSG, Singapore ICT-OUS-4001-1.1 - Applications Support and Enhancement

Blended Learning Journey

(363 Hours)

E-Learning

90 hours

Projects / Assignments

180 hours

Flipped Class/Mentoring

90 hours

Assessment

3 hours

Modules

NICF-Introduction to Python and AI for Data Science (SF)(CRS-Q-0038489-ICT)

You will learn the basics of Artificial Intelligence,  the next generation of software solutions. This provides an overview of AI, and explains how it can be used to build smart apps that help organizations be more efficient and enrich people’s lives. It helps you to take your first steps in the exciting field of AI  using basics of Python programming. Starting from the very beginning, with basic arithmetic and variables, and learn how to handle data structures, such as Python lists, Numpy arrays, and Pandas DataFrames

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, the learner should be able to gain the following knowledge:

  • Machine Learning Concepts
  • Various concepts used in Artificial Intelligence 
  • Statistical Methods in Machine Learning
  • Tools and Methodologies used in Python Programming Language
  • Python Language Fundamentals: basic syntax, variables and types
  • Functions, Packages and Methods used in Python
  • Create and manipulate regular Python Lists
  • Various statistical algorithms that can be applied in Python
  • Basic Plot with Matplotlib 
  • Control flow and Pandas data frame

Skills

By the end of this module, the learner should be able to apply the following skills:

  • Extract, Clean and Transform Data
  • Create Data Models using the transformed data
  • Develop Simple Python application
  • Use functions in Python 
  • Import packages in Python
  • Debug Code to resolve errors in application developed using Python Programming Language
  • Utilize and apply statistical algorithm 
  • Create and customize plots on real data

Other Information

  • Funding Validity Period: Until until 30-July-2021
  • Course Developer: Lithan Academy 

NICF-Applied Machine Learning (SF) (CRS-Q-0038488-ICT)

You will get hands-on experience building and deriving insights of text analytics from machine learning models using Python and Azure Machine Learning.

Learn the essential skills and hands-on experience with the science and research aspects of data science work using Python, from setting up a proper data study to making valid claims and inferences from data experiments.

Get hands-on experience building and deriving insights from machine learning models using Python and Azure Notebooks.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, the learner should be able to gain the following knowledge:

  • Machine Learning and computational modelling techniques used within it
  • Usage of Azure Machine Learning Studio
  • Improvement methods of machine learning models
  • Evaluate Machine Learning Models
  • Planning for Analysis, Power and Simple size planning
  • Learn Research practices
  • Various research claims 
  • Survey design and measurement, Reliability and Validity
  • Correlation and Experimental design

Skills

By the end of this module, the learner should be able to apply the following skills:

  • Develop regression model and classification model
  • Improve Machine Learning models
  • Clean and Validate date using Azure Machine Learning
  • Use optimization based models
  • Apply process in Research and methods of Providing data
  • Perform planning for regression model and classification model
  • Apply Research claims

Other Information

  • Funding Validity Period: Until 30-Jul-2021 
  • Course Developer: Lithan Academy 

NICF-Advanced techniques in Data Analytics (SF) (CRS-Q-0038610-ICT)

You will get hands-on experience building and deriving insights of essential mathematical foundations for machine learning and artificial intelligence using Python. The couse focuses on mathematical concepts that you’ll encounter in studies of machine learning.

Learn to apply ethical and legal frameworks to initiatives in the data profession. You will explore practical approaches to data and analytics problems posed by work in Big Data, Data Science, and AI

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, the learner should be able to gain the following knowledge:

  • Derivatives and optimization  
  • Planning for Analysis, Power and Simple size planning
  • Statistics and Probability
  • Research practices
  • Legal and ethical foundations in Data practice 
  • Bias in Data processing and Data privacy

Skills

By the end of this module, the learner should be able to apply the following skills:

  • Apply System equations and Quadratic equations 
  • Perform sampling distribution 
  • Apply Hypothesis testing 
  • Apply Probability
  • Implement IRAC method to identify legal issue 
  • Apply Classification algorithm to predict recidivism 

Other Information

  • Funding Validity Period: Until 30-Jul-2021 
  • Course Developer: Lithan Academy 

NICF-Deep Learning Foundations (SF) (CRS-Q-0038487-ICT)

You will learn an intuitive approach to building complex models that help machines solve real-world problems with human-like intelligence. The intuitive approaches will be translated into working code with practical problems and hands-on experience. You will learn how to build and derive insights from these models using Python Jupyter notebooks running on your local Windows or Linux machine, or on a virtual machine running on Azure. You will learn how to use the Microsoft Cognitive Toolkit to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.

Session Plan

More Details

Learning Outcome

Knowledge

  • Machine Learning and computational modelling techniques used in Deep Learning
  • Various features within Machine learning and Deep Learning
  • Mathematical models and theory applied in Deep Learning
  • Evaluate Machine Learning Models
  • Multi-Layer perception
  • Convolution Neural Network

Skills

  • Apply Deep Learning Concepts
  • Develop Multi class classification model using Logistic Regression
  • Improve Machine Learning models
  • Use Convolution Neural Network
  • Apply Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM)
  • Perform Text Classification with RNN and LSTM

Other Information

  • Funding Validity Period: Until 30-Jul-2021 
  • Course Developer: Lithan Academy 

NICF-Reinforcement Learning Foundations (SF) (CRS-Q-0038486-ICT)

Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole. You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, the learner should be able to gain the following knowledge:

  • Types of information display using Reinforcement Learning
  • Techniques used in Data visualization with Reinforcement Learning
  • Specification and requirements of RL
  • Gathering, Processing and optimizing accuracy and functionality in Temporal difference Learning
  • Processing multiple streams of data using Deep neural networks

Skills

By the end of this module, the learner should be able to apply the following skills:

  • Reflect trends and correlations of data using RL concepts
  • Develop news recommendations using RL concepts
  • Identify data sources to apply RL concepts in Minecraft game 
  • Perform data exploration in optimal way
  • Apply and implement project Malmo a platform for AI experimentation

Other Information

  • Funding Validity Period: Until 30-Jul-2021 
  • Course Developer: Lithan Academy

NICF-Develop Applied AI Solutions (SF) (CRS-Q-0038485-ICT)

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. In this course, you will be given a thorough overview of Natural Language Processing and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications. We will also discuss deep reinforcement learning techniques applied in NLP and Vision-Language Multimodal Intelligence.

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, the learner should be able to gain the following knowledge:

  • Understanding on various industry developments and trends in Artificial Intelligence
  • Modelling tools used in Natural Language Processing(NLP)
  • Functions and Methods applied in  NLP
  • various algorithms and its use in Artificial Intelligence
  • Deep Reinforcement Learning
  • Models used for Machine Learning and Conversation generation
  • Documentation requirements and protocols in problem management
  • Usage of documentation tools, systems and records to log relevant information throughout the problem's lifecycle

Skills

By the end of this module, the learner should be able to apply the following skills:

  • Apply Functions and Methods in NLP
  • Evaluate algorithms to apply in NLP
  • Evaluate computational methods to apply in NLP
  • Implement Models for NLP
  • Implement Capstone Project  
  • Implement solutions to address the problem through appropriate control procedures
  • Propose solutions to prevent future occurrences of similar problems
  • Document information about problems and the appropriate workarounds and resolutions

Other Information

  • Funding Validity Period: Until 30-Jul-2021 
  • Course Developer: Lithan Academy

Pricing and Funding

SGD 18000.00

Pricing

Fee Description

Detailed Breakdown

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