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Regression VS Classification ML Regression Classification Linear Regression Logistic Regression Cost function MSE Cross Entropy Classification Example Function Loss Linear Regression mse Binary Classification input : x output : yes or no Sigmoid nn.BCELoss() Multi-class Classification input : x or multiple x output : a or b or c... Softmax nn.CrossEntropyLoss() ML Supervised Learning Example Tra..

union It contains all the elements contained in both sets. (A ⋃ B) a={'red', 'green', 'blue'} b=set(['red', 'yellow', 'orange']) a_union_b=a.union(b) print(a_union_b) >>> {'blue', 'green', 'red', 'yellow', 'orange'} intersection It contains only the elements that are in both sets. a_intersec_b=a.intersection(b) print(a_intersec_b) >>> {'red'}

vocabulary_ Mapping all the words with integers. text=["Don't be fooled by the dark sounding name, Mr. Jone's Orphanage is as cheery as cheery goes for a pastry-shop."] vector=CountVectorizer() # Or TfidfVectorizer() print(vector.vocabulary_) >>> {'don': 5, 'be': 1, 'fooled': 6, 'by': 2, 'the': 17, 'dark': 4, 'sounding': 16, 'name': 12, 'mr': 11, 'jone': 10, 'orphanage': 13, 'is': 9, 'as': 0, 'c..

Counter Remove duplicates and return the frequency.Counter('mississippi')>>>Counter({'m': 1, 'i': 4, 's': 4, 'p': 2})Counter(list('mississippi'))>>>Counter({'m': 1, 'i': 4, 's': 4, 'p': 2}) most_common most_common(n)Return a list of the n most common elements and their counts.Counter('abracadabra').most_common(3)>>>[('a', 5), ('b', 2), ('r', 2)] FreqDist Frequency Di..

How to conda update to latest version. $ conda update -n base conda Collecting package metadata (current_repodata.json): done Solving environment: done ... $ conda update --all Collecting package metadata (current_repodata.json): done Solving environment: done ...