ML – the most popular form of AI

Without taking any mathematical notations or too many theoretical details, let's try to approach the term Machine Learning (ML) from an intuitive perspective. For doing this, we will have to take a look at how we actually learn. Do you recollect, at school, when we were taught to identify the parts of speech in a sentence? We were presented with a set of rules to identify the part of the speeches in a sentence. We were given many examples and our teachers in the first place used to identify the parts of speeches in sentences for us to train us effectively so that we could use this learning experience to identify the parts of speeches in sentences that were not taught to us. Moreover, this learning process is fundamentally applicable to anything that we learn.  

What if we could similarly train the machines? What if we could program them in such a way that they could learn from experiences and could start answering questions based on this knowledge? Well, this has already been done, and, knowingly or unknowingly, we are all taking the benefits yielded by this. And this is exactly what ML is when discussed intuitively. For a more formal, standard understanding, let's take a look at the following definition by Tom Mitchell in his book, Machine Learning:

The preceding definition is a more precise version of what we just discussed about ML from an intuitive perspective. It is important to note here that most AI wizardry that we see today is possible due to this form of AI.

We now have a fair idea of what ML is. Now, we will move to the next section, which discusses the most powerful subfield of ML—DL. We will not go into the bone-breaking mathematical details. Instead, we will break it down intuitively, as in this section.