Understanding machine learning and its applications

Machine learning (ML) can be described as a machine’s ability to learn. Let us take an example of the assembly line in a car manufacturing plant. We utilize robotics and very specific instructions are given to the robots to carry out mechanical functions. The robots are machines on an assembly line and instructions are given through embedded programming. This is a kind of artificial intelligence but with very strict use cases. If machines on an assembly line where following an algorithm that gave them the freedom to change or improve their operations by themselves, then that would become an example of machine learning. Because the machines have self-learning capabilities to enhance their style of working. Thus, machine learning is a powerful concept and is driven by different algorithms. We need to carefully evaluate our business needs to understand if ML would be a good fit as ML has specific use cases and in a lot of other scenarios traditional rule-based programming would be sufficient. In this paper, we will explore the different types of machine learning algorithms and how they can be effectively applied to solve problems which could not be solved with existing software engineering approaches.