Linear Regression 1 [Python] 

I'm still trying to wrap my head around the camera/display setup that allows him to write in front of himself but keeps it from looking backwards for us It really adds to the lecture!

Thanks for this Video. I am just starting with Machine Learning and this helped me understanding Linear Regression along with a pretty example. Also a very nice display Setup.

where is the jupyter notebook? cannot find it on website databookuw.com

Hey bro, can you help me out with from sklearn.linear_model import LinearRegression? I get an error message from using. This is the error message I'm getting. from R and python.ModuleNotFoundError Traceback (most recent call last) in () ----> 1 from sklearn.Linear_model import LinearRegression 2 regressor = LinearRegression() 3 regressor.fit(X_train, y_train) ModuleNotFoundError: No module named 'sklearn.Linear_model' Thanks bro.

Do normalizing the data and then applying SVD really make change? I mean does it helps normalizing data before applying SVD?

I understand the reason behind only using some of the data to verify your model, but would it still be 'cheating' to use all of the data to develop a more robust model to predict other homes outside of the data set?

For those experimenting in Mathematica, try these commands---(from PEN) x = 3 delta = .25 a = Range[-2, 2, delta] b = x*a + 1* RandomReal[{-1, 1}, Length[a]] p1 = ListPlot[Transpose[{a, b}], PlotStyle -> Red, PlotLegends -> {"Raw Data"}] p2 = ListLinePlot[{Transpose[{a, x*a}]}, PlotStyle -> Blue, PlotLegends -> {"Exact"}] Show[p1, p2] amat = Transpose[{a}] {U, S, V} = SingularValueDecomposition[amat, MatrixRank[amat]] xtilde = V . Inverse[S] . Transpose[U] . b // First p3 = ListLinePlot[{Transpose[{a, xtilde*a}]}, PlotStyle -> Green, PlotLegends -> {"Regression"}] Show[p1, p2, p3]

Color, LineWidth, MarkerSize these should all be in lower case in the new version of matplotlib

good morning sir