I'm fairly new to Machine Learning field and still to grasp the basics, so this question may seem very stupid, but what is the difference between Machine Learning model, algorithm and hypothesis?
Like the terms are interrelated, but how exactly are they different from each other? They are used interchangeably, so I don't really understand them.
If someone can help to explain these words in simplest terms it would be very helpful.
An algorithm is a sequence of instructions to tell the computer (or a human) what to do. The computer executes the algorithm and produces or not (may not halt) an output (e.g. prints a message). A Python program is an algorithm. Any program is an algorithm. So, think of an algorithm as a recipe that you use to cook/compute something.
A model can have different meanings in different contexts, but they are related. Here I provide more details. For example, in supervised learning, a model can refer to a parametrized function (so kind of a set of functions) or a function. A good model is a model that approximates well some target function (e.g. a function that differentiates between cats and dogs). You need to define good and well, which depends on the context in practice.
A hypothesis is roughly equivalent to the idea of a model. However, this term is used more in computational learning theory. Given your knowledge of ML, I'd ignore this term for now.
Models that run on computers are, by definition, algorithms or part of algorithms. However, it's not always clear why a model (like a neural network) is a sequence of instructions. In fact, in many cases, that's not clear at all. But it turns out that algorithms can compute functions and models approximate, compute or are functions. If you want to think of a model as an algorithm, the best example I know of is a decision tree model.
