Develop your own Machine Learning and Deep Learning models to build value-driven business solutions.
Part-Time / 1.5 Years
Entry to Advanced Diploma in Machine Learning is via Specialist Diploma in Data Analytics (SDDA)
Quizzes, Projects, Continuous Assessments
Data Visualisation and StorytellingThis module discusses the principles and techniques for creating effective visualisations based on graphic design and perceptual psychology. Using widely adopted tools, learners will apply these principles and techniques to create rich visualisations for analysis and presentation. Learners will learn visual analysis techniques to grasp pertinent information, as well as apply exploratory techniques to further derive key insights. Data storytelling and information graphics best practices will also be explored to allow learners to present data effectively and eloquently.
Data WranglingThe aim of this module is to equip participants with the tools and skills sets to handle, clean and prepare large curated data sets for data analytics purposes. Participants of this module should minimally have basic programming knowledge and be able to understand and decipher simple syntaxes. The processed data sets will allow for meaningful statistical analysis, data modelling, and machine learning to be easily performed. Emphasis will be placed on the Extraction, Transformation, and Loading (ETL) of data sets. The use of relevant programming libraries for Missing and Time Series Data will also be explored. Learners will experience the process of exploratory data analysis, normalization of data and data distribution, which will be crucial for subsequent understanding of machine learning concepts and models.
Machine LearningThis module introduces the fundamentals of Machine Learning and its applications. Learners will be provided the essential context and background knowledge of Machine Learning. Learners will gain exposure to both supervised and unsupervised learning models such as Linear & Logistic Regression, Decision Tree, K-means Clustering and more. Using leading software and associated libraries, learners will be able to implement and train Machine Learning models to address business challenges.
Recognition of Prior Learning (RPL)
Applicants who do not meet the entry requirements may be considered for admission to the course based on evidence of at least 5 years of relevant working experience or supporting evidence of competency readiness. Suitable applicants who are shortlisted may have to go through an interview and/or entrance test. The Polytechnic reserves the right to shortlist and admit applicants.
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