Machine Learning and Its Business Applications

Overview

This course is intended to provide learners (students) with an overview of machine learning as well as an introduction to Python as a coding language. It does not get into the statistics of the methods, but rather the methodologies of machine learning. It distinguishes between descriptive statistics and machine learning. Clustering task of data, cluster assessment, data dimensionality, and basics of Python, scikit learn, other learning algorithms, predictive models, scikit learn predictive modelling methods, data generalizability, basics of ensembles, practical limitations of predictive models, supervised (classification), unsupervised (clustering) techniques, and Python coding for analysis are among the other topics covered in this module.

Who Should Attend?

• Company-funded employees who are required to upskill to support the work functions at the workplace.
• Individual who is seeking to upskills for a career progression or employability.
• Individuals who want to acquire the specialised knowledge and qualifications required to explore and enter into a different industry.

Entry Requirement

• 21 years old
• Attainment of IELTS 6.0 or equivalent, or
• A pass in ASCENSUS INSTITUTE’s English Literacy Test with a minimum grade of 70%.
Normal Entry:
• A Bachelor’s Degree in any field or equivalent.

Alternative Entry:
Applicants with the minimum age of 30, who do not possess the above qualifications but have a minimum of 8 years working experience, may be considered for entry into this course.

Evidence of previous employment will need to be provided, together with a reference from the current or recent employer.

Mode of Delivery Blended: (Classroom and Synchronous)
Assessment Framework Combination of Formative and Summative Assessments
Graduation Requirement and Award

Obtained a passing mark with “C-” grade for this course.

Duration

36 hours

Teacher : Student Ratio - 1 : 40
Intakes

Monthly

Fees

Course Fee: S$1,270.94 w/GST (S$1,166.00 w/o GST)

Miscellaneous Fees

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Course Timetable

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Course Application Form

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