wow!!! great video!!!! so informative, the notebooks are a great tool and the graphs help. The libraries are just fantastic, so easy to start and learn machine learning with no knowledge(besides python and few other stuff that) . for me as a high school student it was great video.
1:36:38 - He shuffled the set, he fitted the linear regression and then just checked the score. Shoulden't he predict the y value based on x_train? something like: lr = LinearRegression() lr.fit(X_train, y_train) y_pred_test=lr.predict(X_test) He just checked if the machine was trained but he didn't predict anything :\
+gioolioplus then i tried to visualize how bad the prediction was with this: y_pred_test=lr.predict(X_test) dummyX=np.linspace(0,y_pred_test.shape[0]-1,y_pred_test.shape[0]) plt.plot(dummyX, np.absolute(y_test-y_pred_test), 'o', label="error") Is it correct?
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Thank you so much!!! Gone through so many books but didn't understand much..You guys are awesome...Thanks again for posting!!!!!
For text classification see from 2:54:00
Thanks for uploading this, very useful!
wow!!! great video!!!! so informative, the notebooks are a great tool and the graphs help. The libraries are just fantastic, so easy to start and learn machine learning with no knowledge(besides python and few other stuff that) . for me as a high school student it was great video.
This so wonderful, thanks you so much
Tutorial materials may be found here: github.com/amueller/scipy_2015_sklearn_tutorial
Enthought Awesome - thanks!
Enthought Great! Thanks!
I love enthought! Great work by providing tutorials for free and are very helpful for beginners. Thanks a lot! :D
making that coffee last! Great talk!
Thanks for sharing!
This is the best :D
Does anyone faced issues in audio not clear?! Mine lot of interruptions.
1:36:38 - He shuffled the set, he fitted the linear regression and then just checked the score. Shoulden't he predict the y value based on x_train? something like:
lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred_test=lr.predict(X_test)
He just checked if the machine was trained but he didn't predict anything :\
+gioolioplus then i tried to visualize how bad the prediction was with this:
y_pred_test=lr.predict(X_test)
dummyX=np.linspace(0,y_pred_test.shape[0]-1,y_pred_test.shape[0])
plt.plot(dummyX, np.absolute(y_test-y_pred_test), 'o', label="error")
Is it correct?
please open subtitles