Problem
Supervised learning allows you to collect data from a previous experience such as weather conditions during tornado season for a particular area. This data collection is valuable as it contribute to emergency preparedness. Unsupervised learning can be further understood by the case of the baby and the family dog. The baby becomes familiar with the family dog and understands its' features such as ears, nose, tail and other attributes. When the baby is introduced to other dogs, the learning from the family dog applies and allow the baby to understand that new animal is a dog too.
1. Differentiate between the 3 common unsupervised machine learning algorithms (K-means, DBSCAN, and hierarchal). When should they be used?
2. How are the common types of supervised machine learning algorithms (decision trees, random forest, neural networks, and Naive Bayes) used today?
3. What are two common issues that can arise with the use of each?