[TBD]Role of online learning in data science

Introduction

Laymen explanation

Technical explanation

Challenges of online learning

Optimal Learning rate

One important parameter of online learning systems is how fast they should adapt to changing data: this is called the learning rate. If you set a high learning rate, then your system will rapidly adapt to new data, but it will also tend to quickly forget the old data (you don’t want a spam filter to flag only the latest kinds of spam it was shown).

Data noise impact

A big challenge with online learning is that if bad data is fed to the system, the system’s performance will gradually decline. If we are talking about a live system, your clients will notice.

For example, bad data could come from a malfunctioning sensor on a robot, or from someone spamming a search engine to try to rank high in search results.

Anomalous data impact

The model should also be able to react anomalous data, if any, coming during online learning.

Reference

https://www.amazon.in/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291