As human beings we are intuitively aware of the concept of learning: it simply means to get better at a task over a period of time. The task could be physical (like learning to drive a car) or intellectual (like learning a new language). The subject of machine learning focuses on development of algorithms that can learn as humans do; that is, they get better at a task over a period over time, with experience.
The first question to ask is why we would be interested in development of algorithms that improve their performance over time, with experience. After all, there are many algorithms that are developed and implemented to solve real world problems that don’t improve over time, they simply are developed by humans and implemented in software and they get the job done. From banking to e-commerce and from navigation systems in our cars to landing a spacecraft on the moon, algorithms are everywhere, and, a majority of them do not improve over time. These algorithms simply perform the task they are intended to perform, with some maintenance required from time to time. Why do we need machine learning?
The answer to this question is that for certain tasks it is easier to develop an algorithm that learns/ improves its performance with experience than to develop an algorithm manually. While this might seem unintuitive to the reader at this point, we will build intuition for this during the course of this chapter.
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