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Learn Machine Learning in Python.
Mon, Jan 05, 2015 @ 07:00 PM   $10   AlleyNYC, 500 7th Ave, 17th Fl
 
     
 
 
              

  
 
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Twitter: @NycDataSci


Learn with our NYC Data Science Program(We offered corporate and individual training for more than 40 firms in NYC alone). We offer 12 week immersive program, weekend and weekday night Data Science training.

NYC Data Science Academy launched its 12 week Data Sciencebootcamp. (Apply before Deadline Jan 6th.)

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Vivian will go over the main categories of all the popular algorithms and introduce a few user cases of 3 mostly voted methods. Vote for the methods you want to know the most in the comment section of this meetup.

It will be very hands-on, please bring your laptop and have sklearn installed before hand.

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The options are:

Regression: linear_model.LinearRegression, linear_model.Ridge, linear_model.Lasso, linear_model.ElasticNet

Classification(Discriminant Analysis): lda.LDA qda.QDA

Classification(Tree based model): tree.DecisionTreeClassifier ensemble.RandomForestClassifier

Classification(the others): linear_model.LogisticRegression svm.SVC

Classification(Nearest Neighbors) :neighbors.KNeighborsClassifier neighbors.RadiusNeighborsClassifier

Classification(Naive Bayes): naive_bayes.GaussianNB naive_bayes.MultinomialNB naive_bayes.BernoulliNB

Unsupervised Learning: decomposition.PCA cluster.KMeans cluster.AgglomerativeClustering

Feature Selection: feature_selection.VarianceThreshold feature_selection.SelectKBest feature_selection.SelectPercentile

Cross-Validation: cross_validation.KFold cross_validation.StratifiedKFold cross_validation.cross_val_score cross_validation.train_test_split

Model Selection: linear_model.RidgeCV linear_model.LassoCV linear_model.ElasticNetCV grid_search.GridSearchCV

 
 
 
 
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