I thought I would share my views with everyone who is struggling to get these terms. I will use an analogy that perhaps will help. These opinions are as simplified as I could make them, so please take them with a pinch (or more) of salt.
A coffee maker!Let us have a coffee maker for our thought experiment. We all love some variety of coffee, and there are tons of them. How you brew, how much milk, or none at all, sugar, cream, etc. You get the point.
What if I ask you, yes you, to make the best coffee for all my customers. Now assume you have no idea how to make any kind of coffee. I will supply all the ingredients, and basic tools (a kettle, grinder, and others).
Now I find a whole lot of customers for you. They would come, ask for a type of coffee and you will experiment. Boil the water (or not), add milk (or not), cream (or not), and so on. Oh and vary all kinds of amounts. You will note all your experiments and the result from each customer - trash the coffee or drink + pay. You can see how much they pay, assuming they know what each type of coffee costs and they can add tip if they really like your work.
If you kept doing that experiment over and over again, eventually you will learn how to make good coffee and perhaps of all kinds. That is learning. I did not instruct you how to make coffee, I simply gave you some apparatus, ingredients, and you learnt by seeing the customers response.
The aim of Machine Learning is to enable computers to do the same. There are no clear instructions as to how to do that stuff it is doing, but it figures out by trial and error. Well there is a ton of math involved which is much more complicated than the notes about customers you took, but we will skip that for brevity. We might even mimic biological structures into a computer system to achieve similar ways to learn.
|Coffee Rush by Anders Lejczak|
Experiment notes about customersRemember you took notes about how the customer reacted? What they ordered, if they drank or trashed, how much they paid. You can even add time of day, method of payment, blah blah blah. You get the idea.
Now let's say you wanted to understand which kind of coffee is most profitable. That is going to boost your business. This part is Data Science, where you have data but you are looking for clear signals, which might enable you to take decisions. Now if your log book of customers was huge (say a billion coffee experiments a month) you could use Machine Learning as a technique to have a machine figure out ways to crunch through the data. But that is just one of the ways you could analyse the data. The easiest would be simply looking at it row by row, querying or aggregating it by price range, date, etc. You can use a database to store and do these things.
A coffee maker who plays footballHere we get into very debatable topics. You understand by now that you can learn how to make coffee. From your experience you also know how to learn to drive, drill a hole, use an umbrella, play football, etc. You are still one person, but you have the ability to learn. Some of that is rooted in our deep understanding of language and capability to hold abstract things in our head.
A banana is a banana in your head, you somehow "know" what that means. When you see a picture of a banana, or hear a banana or see even a red banana, you still recognize it even if the color is not common. Cognition is very fundamental to us. It allows us to join abstract (the banana in your head) with the real (pack of bananas at the supermarket).
All these abilities make us flexible to be a driver, farmer, salesman or engineer. What if a machine can do the same? What if it could choose what to learn and gradually get there just like we humans do? Well you are getting to Artificial Intelligence (specifically general AI) - the machine counterpart of our own human intelligence.