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