SKILL BENCHMARK
Fundamentals of Text Processing in NLP Competency (Intermediate Level)
- 21m
- 21 questions
The Fundamentals of Text Processing in NLP Competency (Intermediate Level) benchmark measures your ability to recognize rule-based models for sentiment analysis in Natural Language Processing (NLP). You will be evaluated on your knowledge of representing text as numeric features and using word embeddings to capture relationships in text. A learner who scores high on this benchmark demonstrates that they have good experience in developing text processing applications using NLP.
Topics covered
- analyze and explore review data
- clean data for sentiment analysis
- encode data using term frequency–inverse document frequency (TF-IDF) scores
- encode text as count vectors
- explore bag-of-words and bag-of-ngrams encoding
- filter words based on frequency for classification
- generate Word2Vec text embeddings
- outline how to encode text based on frequencies
- outline how word embeddings work
- perform one-hot encoding on text
- perform sentiment analysis with TextBlob
- perform sentiment analysis with VADER
- provide an overview of sentiment analysis
- stem words and remove stopwords for machine learning
- train a classification model on text embeddings
- train a Gaussian Naive Bayes model on GloVe embeddings
- train classification models for sentiment analysis
- train classification models on n-grams
- train models on TF-IDF encodings
- use the CountVectorizer object for one-hot encoding
- work with pre-trained GloVe embeddings
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