Natural Language Processing with Python and spaCy: A Practical Guide
Build NLP applications with Python and spaCy. Covers tokenization, named entity recognition, dependency parsing, text classification, word vectors, and building custom pipelines.
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# Welcome to Codertronix!
from collections import Counter
def top_words(text, n=2):
words = text.lower().split()
return Counter(words).most_common(n)
print(top_words("Read code write code ship code"))
# Output: [('code', 3), ('read', 1)]Build NLP applications with Python and spaCy. Covers tokenization, named entity recognition, dependency parsing, text classification, word vectors, and building custom pipelines.
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