Unit 7: Data Mining I – Supervised Learning
Data mining attempts to find patterns and relationships within and between given data sets. The field of data mining is vast, so we have broken down its introduction into two units: supervised and unsupervised learning. We will then move on to statistical model-building. When you finish this unit, you will be able to implement learning systems fundamental to the field of data mining.
This unit discusses the basics of supervised learning, feature extraction, dimensionality reduction, and training and testing of supervised learning models. We will focus on benchmark models fundamental to data mining, such as Bayes' decision and K-nearest neighbor. We will implement them using the scikit-learn module. Understanding these methods will prepare you for future excursions into machine learning and deep learning.
Completing this unit should take you approximately 11 hours.
7.1: Data Mining Overview
7.2: Supervised Learning
7.3: Principal Component Analysis
7.4: k-Nearest Neighbors
7.5: Decision Trees
7.6: Logistic Regression
7.7: Training and Testing
Unit 7 Assessment
- Receive a grade