In Chapter 2, Practical Approach to Real-World Supervised Learning, Chapter 3, Unsupervised Machine Learning Techniques, and Chapter 4, Semi-Supervised and Active Learning, we discussed various techniques of classification, clustering, outlier detection, semi-supervised, and active learning. The form of learning done from existing or historic data is traditionally known as batch learning.
All of these algorithms or techniques assume three things, namely:
Finite training data is available to build different models.
The learned model will be static; that is, patterns won't change.
The data distribution also will remain the same.
In many real-world data scenarios, there is either no training data available a priori or the data is dynamic in nature; that is, changes continuously with respect to time. Many real-world applications may also have data which has a transient nature to it and comes in high velocity or volume such as IoT sensor information, network...