Polynomial regression 

Thank you kindly, Drew.

Thank you, kind internet stranger.

You got it, bro

Hi Philipp. There are many ways to simulate noise, depending on the goal. The easiest thing to do is to use white noise (randn in MATLAB or np.random.randn in Python) with a suitable standard deviation.

​@@mikexcohen1 Thank you for the reply. What I am looking at is a neural network that approximates a polynomial given x values as input and their associated function values (y) as the label. I want to test the robustness of the model by adding noise to the function values of the training data. Would you say that the use of a normal distribution with a suitable standard deviation and the original, "true" y as the mean is appropriate for this endeavor?

@@philippu1455 yeah that should work

Glad you enjoyed it :)

Looking for that too

Udemy Mike Cohen, Machine Learning Course

Thank you

The null hypothesis of regressors in a model is always the same: That the coefficient (the beta value) is statistically indistinguishable from zero.

Welcome to the team ;)

Not in this video, but in the full course, yes, there are examples in Python code with explanations.

It's actually the mse

No, you're awesome! ... well, let's both be awesome ;)

I think it's MSE only

Yikes! Yes, you're correct, and it's a bit of a typo there. I guess I was mixing code and math while writing out that equation. Anyway, my apologies for the confusion, and good catch!

@@mikexcohen1 All good :) like your video !!!

If memory serves, in some math books, e is the default for a log base instead of 10, i.e. "log" is used for "ln".