Who is learning python or c++ for machine learning?

Question

I just want to start the thread for machine learning enthusiastic people.

Answer 1

>>> from sklearn import svm
>>> X = [[ 0 , 0 ], [ 1 , 1 ]]
>>> y = [ 0 , 1 ]
>>> clf = svm . SVC()
>>> clf. fit(X, y)

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)

SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical formulations. On the other hand, LinearSVC is another implementation of Support Vector Classification for the case of a linear kernel. Note that LinearSVC does not accept keyword kernel , as this is assumed to be linear. It also lacks some of the members of SVC and NuSVC , like support_ . As other classifiers, SVC , NuSVC and LinearSVC take as input two arrays: an array X of size
[n_samples, n_features] holding the training samples, and an array y of class labels (strings or integers

Do you have an email?

Answer 2

cool, I just started to work on machine learning. do you experience in this field?

Answer 3

nice, these all things are new for me. I worked baysian classifier. Now, I start to work on svm classifier.

Answer 4

Thanks for response.
currently, I am working on hand detection from depth data by machine learning. so, I am studying about svm classifier. It will be helpful, if you have any idea about this.
thank you.

Answer 5

I'll email you on my thoughts

Answer 6

Python ML... I'm learning it

Answer 7

yeah ☺

Answer 8

Yeah for statistical analysis and sentiment analysis using nltk, scikit-learn, matplotlib for graphing varience, and regression plots and pybrain... what about you?

Answer 9

great✌️