AI consists of tasks that are characteristic of human intelligence, such as language and speech understanding, recognizing objects and sounds, and planning. The word tasks is used because, technically, you can complete these tasks with two different approaches: classical programming and ML.

At the beginning of 1990, some companies introduced Optical Character Recognition (OCR) software. They invested millions of dollars and hired hundreds of developers who wrote code to recognize handwritten text. This was classical programming with simple tools, such as if-else. The approach worked, but the result was quite poor. The accuracy was low and the number of mistakes was high.

Why did this approach fail? Because what is natural for a human brain is very difficult to be programmed.

So, how we can solve this task in another way? The answer is ML!

Or, in other words: computers learn from data to perform predictive analytics.

As you can see, instead of writing code, to create ML functions, we need something else: data.

So, let's cover this process in detail in the following sections.