Q.7 Machine learning algorithms are described as learning a...

Q.7 Machine learning algorithms are described as learning a ______(f) that best maps input variables (X) to an output variable (Y) Y = f(X). 1.0 target 2.0 function 3.0 target function 4.0 model

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00:01 The first couple problems aren't too bad.

00:03 I don't know if you have a question on that, but what happens for a quadratic if it's written in the equation, so the equation form of a quadratic, the highest exponent, or largest exponent, maybe it would be a better word, is two.

00:26 So if you look at number one where, what are we looking at, 8x plus 5, that is not quadratic because the highest exponent is 1.

00:43 And if you're curious, it is actually linear.

00:50 It's a linear thing.

00:51 So i think that's all you have to say is that it's not quadratic.

00:53 But anyway, number two is it quadratic.

00:58 I don't really care about everything that's happening there.

01:00 I just care about the x squared.

01:03 But yeah, minus 2x plus 7.

01:07 So this one is.

01:08 So yes, quadratic.

01:13 Now, the next one's a little bit trickier because what happens is you're going from what is it, negative 5, negative 4, negative 3.

01:23 We actually don't really care about the x values, but it is important that you're going up by 1 each time.

01:29 Otherwise, it gets trickier.

01:31 So, again, we don't really care about those x values.

01:34 But we do care about the f of x values or y values...

Question

Q.7 Machine learning algorithms are described as learning a ______(f) that best maps input variables (X) to an output variable (Y) Y = f(X). 1.0 target 2.0 function 3.0 target function 4.0 model

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