Summary
In this chapter, you have learned the basics of online machine learning in both theory and practice. You have seen different types of online machine learning, including incremental, adaptive, and reinforcement learning.
You have seen a number of advantages and disadvantages of online machine learning. Among other reasons, you may be almost obliged to refer to online methods if quick relearning is required. A disadvantage is that fewer methods are commonly available, as batch learning remains the industry standard for now.
Finally, you have started practicing and implementing online machine learning through a Python example on the well-known iris dataset.
In the coming chapter, you'll go much deeper into online machine learning, focusing on anomaly detection. You'll see how machine learning can be used to replace the fixed rule alerting system that was built in previous chapters. In the chapters after that, you'll learn more about online classification...
