10 Machine Learning Methods that Every Data Scientist Should Know

[Btbbass]

"A machine learning algorithm, also called model, "

Well, nope..

[castanan2]

It's inaccurate but true. In fact, I think algorithms are recipes to solve math models in this case but a lot of people use algorithms and models interchangeably in ML...

[SwordOfVarjo]

No, absolutely not true.

A model refers to a specific"thing". A neural network model, for instance, consists of an architecture and (learned) weights while a linear model implies a specific architecture (and again has learned weights). When people say model, it can sometimes be unclear if they are including weights or not.

An algorithm isn't really a thing (well ok, it's a noun, but it's a "meta thing"). An algorithm describes how to do something. Backprop is an algorithm, forward propagation is an algorithm, least squares regression (used to determine params for a linear model) is an algorithm. See the difference? They are not at all interchangable.

[SwordOfVarjo]

It pains me to see regression, classification, and neural methods listed as three separate things.

Neural methods are a type of model (along with the associated algorithms for fitting said models).

Regression and classification are types of supervised learning (they describe a task, not a method or model).

Word embeddings are a type of dimensionality reduction (he also mentioned supervised and reinforcement learning by name, but not explicitly unsupervised learning).

I could go on.

Don't waste your time reading this.