Professional Certificate in Machine Learning and Data Visualization
At the completion of this module, students will be able to:
• Understand fundamental and practical challenges of human factors in machine learning
• Understanding the pros and cons involved in human-computer design choices
• Design visual representations and human interactions for exploring and interpreting machine learning models and systems
• Create interactive data visualization tools for supporting various machine learning tasks
• Understand the common algorithms and data structures used in information technology
• Evaluate the usability and utility of data visualization for machine learning.
• Understand the fundamentals of database systems, including the logical and physical design in a relational database.
In this course, students will learn the skills to implement a relational database, and to use SQL statements to query database systems. Students will also learn the common algorithms and data structures that are of central importance to many applications in data analytic. Additionally, they will also apply selected AI and machine learning/deep learning algorithms to solve real world problems and use machine learning tools to build ML models for prediction and classification tasks. This course will also cover data preprocessing, and statistical techniques such as linear regression and multivariate regression for model prediction. Last but not least, Students will learn to use R and RStudio for exploratory data analysis and visualization, and understand the common pitfalls in data visualization, the key features of human computer interaction and common interaction styles & also the best practices for data visualization and story-telling.
1. Database System II
2. AI and Machine Learning I
3. AI and Machine Learning II
4. Data Visualization Using R
5. Human Computer Interaction
6. Algorithms and Data Structures I
Project Duration
Part-time Study: 6 months (108 hours in total - excluding of lunch hour)
Mode of Delivery
Lectures, discussions, role play, E-learning, self-study, face-face tutorials, practicals.
Trainer Support
Students will receive guidance and instruction by highly qualified professors and lecturers from reputable Australian and Singaporean universities.
This course is designed for students to acquire the relevant skillsets to build data-driven Machine Learning / AI applications.
Students will develop the relevant skillsets to build data-driven Machine Learning/ AI applications and cognitive products using Python. Students will also learn about algorithm analysis, basic data types such as stacks, queues and trees and how to implement them using a programming language. Students will also learn to wrangle data and apply visualization techniques with Python. Additionally, students will also learn to use R to manage and pre-process data, perform data analytics and develop analytic models for the visual representation of data, as well as human computer design choices and various visualization techniques for effective communication of information through graphical means. After completing the course, students will be able to develop intelligent applications that harness the power of AI to help them gain a competitive edge.
- Aged 18 years old and above
- Academic performance with at least 1 credit in SPM/UEC or any other equivalent qualification.
- Admission to this program is based on the performance of student admission interview.
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