Data Mining Course

Day 1:

Module 1:

To enable learners to have an overall understanding of the landscape of Data Mining and Machine Learning.

• Introduction: Machine Learning and Data Mining
• Supervised and Unsupervised learning techniques

Module 2:

To give the participants the very first steps towards forming hypothesis and doing predictive analytics, learning some of the classification and regression techniques in supervised learning.

• Machine Learning and Classification
• Classification: Logistic Regression, K-Nearest Neighbour.
• Regression: Multiple Linear Regression

Module 3:

It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out.

• Evaluation and Metrics
• Understanding and comparing model results

Day 2:

We will continue the journey for the learners in understanding some of the preparation and visualisation techniques which is required for knowledge discovery into their data, on this day unsupervised learning techniques will also be taught.

Module 4:

To equip learners in understanding some of the preparation and visualisation techniques which is required for knowledge discovery into their data.

• Data Preparation for Knowledge Discovery
• Data Mining and Visualization

Module 5:

To further equip learners with knowledge on other unsupervised learning techniques, and rule-based mining also be taught.

• Clustering
• Associations Rules