Analytic & Ensemble Machine Learning
About This Course
This course offers a foundational overview of machine learning, examining both analytic and ensemble approaches. Beginning with basic concepts, the course then progresses to linear regression, classification, kernel-based techniques. Learners will then explore ensemble approaches like bagging and boosting, including classic algorithms like Random Forest, AdaBoost, and traditional Gradient Boosting before advancing to the more popular eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The curriculum also introduces reinforcement learning and its relationship to ensemble methods, with practical industry examples. Learners can enhance employability and position themselves for roles in Artificial Intelligence (AI) and machine learning engineering upon the completion of this course.
As this micro-credential course is only offered in the FlexiMasters in Signal Processing and Machine Learning programme, learners are required to enrol into the programme and complete all required courses within this programme.
What You'll Learn
• Implement and use data preprocessing, linear models and learning score functions.
• Implement and use analytic regression, classification and kernel methods.
• Implement bagging framework and random forest algorithms to solve classification and regression problems.
• Describe and evaluate the boosting framework, various boosting algorithms, and reinforcement learning.
Entry Requirements
No pre-requisites are needed to enroll in the individual courses; however, learners without qualifications or working experience in relevant engineering or the related fields may find the course contents challenging.
Note: Shortlisting will be conducted.