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- Predicting new utterances using our model
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Classroom Contents
Complete Natural Language Processing Tutorial in Python
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- 1 - Announcements!
- 2 - Video overview & timeline
- 3 - Bag of words BOW overview
- 4 - Bag of words example code! sklearn | CountVectorizer, fit_transform
- 5 - Building a text classification model using bag-of-words SVM
- 6 - Predicting new utterances classes using our model transform
- 7 - Unigram, bigram, ngrams using consecutive words in your model
- 8 - Word vectors overview
- 9 - Word vectors example code! Using spaCy library
- 10 - Building a text classification model using word vectors
- 11 - Predicting new utterances using our model
- 12 - Regexes pattern matching in Python.
- 13 - Stemming/Lemmatization in Python text normalization w/ NLTK library
- 14 - Stopwords Removal removing most common words from sentences
- 15 - Various other techniques spell correction, sentiment analysis, part-of-speech tagging.
- 16 - Recurrent Neural Networks RNNs for text classification
- 17 - Transformer architectures attention is all you need
- 18 - Writing Python code to leverage transformers BERT | spacy-transformers
- 19 - Writing a classification model using transformers/BERT
- 20 - Fine-tuning transformer models
- 21 - Bring it all together and build a high performance model to classify the categories of Amazon reviews!
