Note: This video was recorded using Python 2.7 and scikit-learn 0.16. Recently, I updated the code to use Python 3.6 and scikit-learn 0.19.1. You can download the updated code here: https://github.com/justmarkham/scikit-learn-videos
you're the right model for us , because u trained such a way
hi bro Accuracy should be 100% because we are training same datamodel
Thank you very much again! I look forward to learning mode on the various libraries and models for machine -learning, with great examples as usual. Greetings!
do you have a full machine learning python course i can do ? post the link please
Great Tutorial… Really very helpful. Very well explained. Thank u very much.
Love you..You are awesome..Keep going
You gotta superb way of explaining things in a very simple manner. Request you to share one detail video on data visualization in pandas and one complex data science project.
Such a clear and concise explanation. Liked and sub'd.
I am new in machine learning and now I am facing an issue. I have 7 projects I would like to predict whether a pull request would be rejected or not (Yes or No). And I would like to build a prediction model by using data from 6 projects as source project and predict the rejection of the pull request in the seventh project as a target project. Can you please tell me how can I structure my algorithm in Scikit-learn? Hope that my question is clear.Thanks
great video. best of the best
I ADORE YOU 💕💕💕 you teach difficult concepts really well. Thank you and i hope you keep posting.
Hi Kevin,Do you have any blog which you continuously update to stay in touch with your latest info on machine learning/Data science articles ?
I don't understand why eventually we fit the whole dataset into the model after we get the best k value after we use training and testing dataset?
Great Teaching and Material !!
Really simple to understand. Doesn't make it seem like "its a library thing, library does it for ya". Thank you for doing this
OMG finally found a ML tutor who is awesome…. i cant skip any seconds in your videos, even each words are informative
Thank u a lot for the tutorials
Your videos are so cool and easy to understand, thanks for uploading,please keep on doing it!
Is there a particular reason to choose the middle of the range of optimal K (22:10), or would there be cases where we could want to take the lower/high (6~17) value of K in the range of optimal accuracy, after plotting K and model accuracy? Thank you!
Your teaching style is outstanding. As someone who has used R in the past, I really appreciate the clarity of your explanations and demonstrations.
Great series of courses on Pandas and Scikit Learn! I’ve been enjoying every video I watched on this channel. Thanks so much!
On machine learning using Scikit Learn, I’m wondering if you could share a lesson on Random Forest and related concepts. Thanks again.
Wonderful presentation, very helpful!
how to deal with 3d medical images dats sets and suppose I have a 3d image then how i can test that image to get the categorical result
i love everything about the series. Thank you very much
Thanks so much for making such amazing material, i am more motivated that i can further work in data science. kudos bro
Thank you very much for your valuable time explaining machine learning. I will always think how they predict the future value. Now I know.
Bro, you're a god!
Thank you Kevin! One question, the returned value on RMSE, that was in units of y, correct?
There are so many parameters within each model to tune. So how to decide 1. On which and On how many different parameters to tune for our problem statement and 2. On how many different models to try before finalising the model.
Awesome …. Can you post some videos on using random forecast and SVM techniques with examples
Thanks a lot :). Very HelpFul
Dude, thank you very much for this set of videos. I'm from Brazil and I'm really enjoying youe course.
Great videos Sir. But i have one question i.e. how come the accuracy is not 100% since we are predicting on the same features X that we fit it with : logreg.fit(X,y)y_pred = logreg.predict(X)from sklearn import metricsprint(metrics.accuracy_score(y, y_pred))0.96
Great video….very helpful…
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