Walden University Programming Worksheet

I have a Computer Science live assignment on the 4th of January at 12 PM EST and it is based on python.

The time limit is 3 hours long.

I have attached the units

I have also attached the practice live assignment, which will be very similar to the actual live assignment for January 4th. I have also attached a previous lab we did and the lab description.

Lab 1.1: Intro to python
Lab 1.2: Functions And Sets
Lab 2.1: Preprocessing Text, Tokenization, random sampling of sentences
Lab 2.2: Normalising, Number and case, stemming & lemmatization, punctuation and
stopword removal.
Lab 2.3: Regular Expressions
Lab 3.1: Basic Document Classification, creating training and testing sets from data, creating
bag-of-words representations using FreqDist, creating word lists, creating word list based
classifier, using classifier on test data
Lab 3.2: Calculating accuracy of a classifier, getting the train and test data, precision, recall,
f1 score, graphs to store results
Lab 4.1: constructing a Naive Bayes classifier, creates lists, class priors, conditional
probability of a document, add one smoothing, known vocabulary, underflow
Lab 4.2: Evaluating NB classifier on test data, NLTK nb classifier
Lab 6.1: Document similarity, measuring similarity, cosine similarity, beyond frequency,
Lab 7.1: Lexical semantics, navigating wordnet, synsets for PoS, distance to roots, semantic
similarity in wordnet, resnik and lin similarity scores, scatter plots comparing resnik, lin
similarity to human similarity.
Lab 8.1: Distributional semantics, most frequent, generating feature representations, PMI,
positiver PMI & Vectors, word similarity, nearest neighbor,
Lab 9.1: PoS tagging, average PoS tag ambiguity, freqDist of tags for every word in input,
Entropy as a measure tag of ambiguity, simple unigram tagger, beyond unigram tagging,
hidden markov model tagger
Lab 10.1: Named Entity Recognition, SpaCy, make tag lists, extracting entities,
Lab 11.1: Info retrieval, Question and answering, SQUAD datatset, keyword search,
docsearch, keyword index, ranking documents, tf-idf,
Weekly content
Complete all items
D
Week 1: Intro to NLE and Python
Mark completed
Week 2: Text Documents and Preprocessing
Mark completed
Week 3: Document classification
Mark completed
Week 4: Further document classification
Mark completed
DWeek 5: Consolidation
D
Week 6: Document Similarity and Clustering
Mark completed
Week 7: Lexical Semantics and Word Senses
Mark completed
O
D
Week 8: Distributional semantics
Mark completed
D
Week 9: Part-of-speech tagging and Hidden Markov Models
Mark completed
D
Week 10: Named entity recognition (NER) and information extraction (IE)
Mark completed
Week 11: Question answering (QA)
Mark completed