Unit Content
Outcomes
- Evaluate different data mining techniques, such as clustering, classification, and association rule mining, to extract valuable patterns and insights from large datasets;
- Employ machine learning algorithms for tasks like regression, classification, and unsupervised learning, enabling data-driven decision-making;
- Critically evaluate the performance of machine learning models using appropriate metrics;
- Discuss the application of data mining and machine learning to techniques to solve real-world business problems.
Subject Content
- Introduction to Data Mining and Machine Learning
- Data Preprocessing and Cleaning
- Supervised Learning Algorithms
- Unsupervised Learning and Clustering
- Dimensionality Reduction Techniques
- Real-World Applications and Case Studies
- Feature Selection and Engineering
- Evaluation Metrics for Machine Learning Models
- Ensemble Learning and Model Stacking
- Time Series Analysis and Forecasting
- Text Mining and Natural Language Processing
- Deep Learning and Neural Networks
This course may be offered in the following formats
- Face to face on site
- E-learning (online)
- Intensive
- Extensive
Please consult your course prospectus or enquire about how and when this course will be offered next at Alphacrucis University College.
Assessment Methods
- Exam (25%)
- Maching Learning Project and Report (50%)
- Group Case Study (25%)
Prescribed Text
Raja, R., Nagwanshi, K. K., Kumar, S., & Ramya Laxmi, K. (2022). Data Mining and Machine Learning Applications. Wiley-Scrivener.
Check with the instructor each semester before purchasing any textbooks
