Product
Professional Diploma in Artificial Intelligence
SCTP-Professional Diploma in Artificial Intelligence (Synchronous and Asynchronous E-Learning)
9 months Part time / 6 Months Full time Instructor-led Live & Mentor-led Blended Learning
Become a skilled professional in Artificial Intelligence and design trending AI Solutions using Machine Learning, NLP, Computer Vision and Deep Learning.
What do I Get?
Develop the Skill of Exploratory Data Analysis
Learn to create python programs and implement statistical concepts in Machine Learning models using visualization techniques such as basic plot and Matplotlib.
Absorb the Fundamentals of Machine Learning and Artificial Intelligence
Learn about the foundations of Machine Learning and Artificial intelligence, understand Regression , Classification and Clustering Algorithms and attain the skill of implementing them in creating precision based models in Azure Machine Learning Studio.
Understand the Concepts of Deep Learning
Learn and understand the Deep Learning environment and interface, obtain skills to implement computational modelling techniques and various features in machine learning and deep learning.
Gain Knowledge on Reinforcement Learning Framework
Learn the basics of Reinforcement Learning and its applications, understand the Bandit Framework and the reinforcement Learning problem.Obtain skills of Data Exploration and develop new recommendations using RL concepts by learning techniques used in data visualization with RL.
Learn and Develop Applied AI Solutions
Gain knowledge on NLP and learn the basics of NLP using classic machine learning methods, functions and algorithms in AI to implement the skills in cutting edge learning methods..
Audience and Certificates
Target Audience
- IT professionals whose goal is to become an AI Engineer and develop intelligent applications can take up this course
Prerequisite
Minimum Age : 21,
Academic Level : Minimum 3 GCE A Level passes or its equivalent,
Work Experience : Minimum 1 year experience statistics or programming,
Language Proficiency : IELTS 5.5 or its equivalent.
Graduation Requirements
- Minimum attendance of 75% for all the sessions in each module of the course
- Should be assessed as ‘Competent (C)’ in each module of the course
Certificate(s)
- Professional Diploma in Artificial Intelligence (E-Learning)
- 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
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 (Bundled) (SF) (Synchronous and Asynchronous E-learning) (TGS-2019503549)
Apply the concepts of python programming like the basic arithmetic and variables, data structures such as Python lists, Numpy arrays, and Pandas DataFrames to initiate the learning process in the vast and interesting field of AI. Learn the basics and advanced solution of AI field helping build smart applications for current edge productivity.
Session Plan
Learning Outcome
Knowledge
By the end of this module, you will gain following knowledge:
- Python Language Fundamentals such as basic syntax, variables and data types
- Execute arguments, 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 concepts
- Control flow and Pandas data frameMachine Learning Concepts
- Critical concepts used in Artificial Intelligence
- Statistical Methods in Machine Learning
Skills
By the end of this module, you will acquire following skills:
- Import and Extract, Clean and Transform Data using programming
- Create Data Models using the train and test data
- Develop a Python application with the relevant dataset
- Use functions in Python
- Import packages in Python
- Resolve errors by debugging through the application developed using Python
- Utilize and apply statistical algorithm
- Create and customize plots on real data
- Learn to implement the Machine learning models
- Create applications based on AI solutions
Other Information
Funding Validity Period: Until 31-Dec-2022
Course Developer: Lithan Academy
NICF-Applied Machine Learning (Bundled) (SF) (Synchronous and Asynchronous E-learning) (TGS-2019503550)
Build experience and deriving insights on Machine Learning and computational modelling techniques. Learn skills and hands-on experience on the technology and the research prospect of data science space around Python, by getting solid inferences from data experiments by using optimized models for best results.
Session Plan
Learning Outcome
Knowledge
By the end of this module, you will gain following knowledge:
- Machine Learning and computational modelling techniques including the supervised and unsupervised algorithms
- Usage of Azure Machine Learning Studio
- Optimization of the exsisting models for best results
- Improvement methods of machine learning models
- Evaluate different machine learning models
- Planning for analysis, power and simple size planning
- Learn research practices
- Data exploration and cleansing
- Learn Featureengineering
Skills
By the end of this module, you will acquire following skills:
- Develop regression model and classification model
- Improve Machine Learning models
- Clean and validate date using Azure Machine Learning
- Use Optimization based models for best results
- Apply process in research and methods of providing data
- Perform planning for regression model and classification model
- Apply tuning process for better understanding of the hypermeter tunning for testing train and test data.
Other Information
Funding Validity Period: Until 31-Dec-2022
Course Developer: Lithan Academy
NICF-Advanced Techniques in Data Analytics (Bundled) (SF) (Synchronous and Asynchronous E-learning) (TGS-2019503551)
Get hands-on experience in designing and deriving insights from essential Text Analytics for machine learning and artificial intelligence using Python. Learn to apply operations on Regression models and use Linear regression for prediction and forecasting.Explore practical approaches to data and analytics problems in the field of Big Data, Data Science, and AI.
Session Plan
Learning Outcome
Knowledge
By the end of this module, you will gain following knowledge:
- Operation of Classifiers and how to use Logistic Regression as a Classifier
- Metrics used to evaluate classifiers and regression models
- Operation of Regression models and how to use Linear regression for prediction and forecasting
- Problems of over-parameterization and dimensionality
- Use common supervised machine learning models
- Compare different Multi Class models to analyse the best model
- Work on Text analytics solutions
Skills
By the end of this module, you will acquire following skills:
- Identify text analytics solution and platform requirements
- Define the metadata and corpus for the data to be imported into the text analytics repository
- Develop a standardised set of text analytics artifactswith the relevant stakeholders
- Develop term-document frequency matrix to enable lookup of text and documents within the corpus
- Modify the text analytics solution to ensure that it produces the expected results
- Define the process to perform text analytics based on the business requirements and text analytics artifacts
- Use regularization on over-parameterized models
- Apply cross validation to estimating model performance
- Evaluate k-means and hierarchical clustering models
- Apply Machine Learning models to real-life situations
Other Information
Funding Validity Period: Until 31-Dec-2022
Course Developer: Lithan Academy
NICF-Deep Learning Foundations (Bundled) (SF) (Synchronous and Asynchronous E-learning) (TGS-2019503552)
Learn the innovative approach to building complex models using the Deep learning skills that help machines solve real-world problems with human-like intelligence through working code with practical problems and hands-on experience. Implement and learn to harness the intelligence using the Microsoft Cognitive Toolkit within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.
Session Plan
Learning Outcome
Knowledge
By the end of this module, you will gain following knowledge:
- Machine Learning and computational modelling techniques used in Deep Learning
- Deep Learning as a subset of data science
- Essential features within Machine learning and Deep Learning
- Mathematical models and theory applied in Deep Learning
- Evaluate the Machine Learning Models in terms of important parameters like accuracy and precision
- Learn and apply Multi-Layer perception
- Concepts of Convolution Neural Network (CNN)
Skills
By the end of this module, you will acquire following skills:
- Apply Machine learning and Deep Learning Concepts
- Develop Multi class classification model using Logistic Regression
- Apply Hypertuning to the Machine Learning models for best results
- Apply and use the Convolution Neural Network approach for deep leraning
- Apply Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM)
- Perform Text Classification with RNN and LSTM
Other Information
Funding Validity Period: Until 31-Dec-2022
Course Developer: Lithan Academy
NICF-Reinforcement Learning Foundations (Bundled) (SF) (Synchronous and Asynchronous E-Learning) (TGS-2019503553)
Attain competency and learn to tackle reinforcement learning problems and start working with examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole. Explore the basic algorithms from multi-armed bandits, dynamic programming, Temporal difference learning and progress towards larger state space using function approximation.
Session Plan
Learning Outcome
Knowledge
By the end of this module, you will gain following knowledge:
- Types of information display using Reinforcement Learning
- Techniques used in Data visualization with Reinforcement Learning
- Specification and requirements of Reinforcement learning
- 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, you will acquire following skills:
- Adapt and work on reflect trends and correlations of data using RL concepts
- Develop news recommendations using RL concepts
- Identify data sources and develope 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 31-Dec-2022
Course Developer: Lithan Academy
NICF-Develop Applied AI Solutions (Bundled) (SF) (Synchronous and Asynchronous E-Learning) (TGS-2019503554)
Work on the Natural Language Processing concepts using classic machine learning methods and cutting-edge deep learning methods.Learn to apply the Neural models for machine translation and conversation that works on Statistical Machine Translation and neural models for translation and conversation.
Session Plan
Learning Outcome
Knowledge
By the end of this module, you will gain following knowledge:
- 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
Skills
By the end of this module, you will acquire 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 a Capstone Project
- Implement solutions to address a problem through appropriate control procedures
- Propose solutions to prevent future occurrences of similar problems
Other Information
Funding Validity Period: Until 31-Dec-2022
Course Developer: Lithan Academy
