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Diploma in Machine Learning Essentials - FREE for unemployed, formerly self-employed and ‘Returners

-Created 08-12-2020


location Online, evenings

Course Title:   Diploma in Machine Learning Essentials - FREE for unemployed, formerly self-employed and ‘Returners
Course Category:   IT
Duration:   15 weeks
Entry Requirements
Admission to this modular programme is through one of the following:

Evidence of prior learning, including experiential learning,


Possession of an NFQ level 5 award, including leaving certificate, FET award, or equivalent.

In addition, all applicants must evidence competence in mathematics equivalent to O6 standard in leaving certificate and competence in the use of IT. Basic computer literacy is not sufficient for this programme.

International applicants whose first language isn’t English must demonstrate a minimum competency in the English Language of CEFR B2+.

Applicants are encouraged to apply for entry based on prior learning (RPL) or prior experiential learning (RPEL) in line with the College policy. The College will thoroughly assess applications received through RPL and RPEL to ensure that candidates are able to evidence learning to an appropriate standard – normally the framework level equivalent to the direct entry qualification requirement and demonstrate potential to succeed and benefit from the programme.
Course Content
Digital Transversal Skills
The aim of this module is to provide the learner with understanding of:
1. Digital tools and technologies that are used in modern businesses for high productivity, with consideration for legal, ethical and privacy issues.
2. The methods and approaches to managing goals and tasks within a multicultural
team setting, incorporating effective collaborative and interactive communication strategies.
3. The importance of self-reflection and continuing professional development to further one’s own personal and professional development.

Introduction to Programming for Data Analytics
The aim of this module is to provide the learner with knowledge of:
1. Fundamental programming concepts
2. Problem solving techniques in the context of programming
3. Basic data manipulation operations
4. The importance of program documentation
5. Common tools used for programming for data analytics

Machine Learning I
The aim of this module is to provide the learner with:
1. The role of machine learning as a tool to solve data analytics problems.
2. The purpose of data mining frameworks and their usefulness.
3. The distinction between supervised and unsupervised machine learning methods.
4. The difference between the two fundamental types of supervised learning – classification and regression.
5. Software tools used to solve classification and regression problems.

Machine Learning II
The aim of this module is to provide the learner with understanding of:
1. The principles underlying unsupervised machine learning techniques, e.g. cluster analysis, anomaly detection and market basket analysis.
2. Machine learning approaches to text analytics – natural
language processing, sentiment analysis, topic modelling, text classification and document summarisation.
3. Approaches to the analysis and modelling of temporal data.
4. Software tools for unsupervised learning, text analytics and time-series modelling.

The Machine Learning modules on this course will equip the learner with the skills necessary to tackle a wide range of unsupervised learning problems, such as cluster analysis and text analytics. Both techniques are widely used in the analysis of business data as they allow the enterprise to develop a deeper understanding of their customers. These modules build on and draw from the Introduction to Programming for Data Analytics module to equip the learner with practical experience of the use of commonplace classification and regression approaches. The Digital Transversal skills module will empower the learner using both collaborative tools and techniques and individual insights into problem solving in a digital domain with a focus on the workplace.

This modular course will appeal to a range of adult learners who may or may not be currently in employment and wish to up-skill, or returners and other groups who may wish to re-skill and are seeking to develop their knowledge in machine learning applications on a flexible modular basis. It will give those working in IT, marketing, finance and other industries a knowledge of machine learning which will help enable them to better manage their data for improved business forecasting and decision making.
Learning Outcomes
This course will be delivered online. Online activities can include live or pre-recorded lectures, independent learning and assessment activities such as research tasks, discussion forums, simulations, quizzes and e-portfolio work along with online group activities such as live classes, group project work, virtual labs and tutorials.

Awarding Body
Awarding Body:   QQI
Start Date
Start Date:   Week beginning 25/01/2021
Course provider information
Contact person:   CCT College Dublin
Course Email:
Contact:   +353 1 6333444
Institute:   CCT College
Location:   Online, evenings

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