Machine Learning Applications for Predictive Analytics (ANL308)

ANL308 Machine Learning Applications for Predictive Analytics equips students with the skills to develop supervised machine learning models for solving real-world problems. The course covers key concepts such as model construction, evaluation, selection, and deployment, ensuring students understand how to build and assess effective models under various application scenarios. The course also introduces deep learning techniques, enabling students to apply advanced methods for complex predictive tasks. By the end of the course, students will be proficient in conducting a supervised machine learning solution for real-world cases using Python and cloud services.

• Introduction to Predictive Modelling
• Applications of Predictive Modelling
• Model Construction and Evaluation
• Model Selection
• Logistic Regression for making predictions
• Decision Trees: CHAID and CART
• Decision Trees: QUEST and C5.0
• Artificial Neural Networks
• Ensemble Models
• Random Forest
• Support Vector Machine (SVM)
• Applying Predictive Analytics on Cloud Services

• Discuss various aspects of applying machine learning for predictive analytics
• Appraise the application of predictive analytics
• Compare different machine learning methods for predictive analytics
• Construct predictive models/results, including the use of AI-assisted approaches, as part of solutions to address business problems
• Evaluate the performance of predictive models
• Interpret the results or outputs of predictive models for addressing business problems
• Verify the validity, reliability and limitations of AI-assisted predictive models and outputs