Word Embedding for Twitter Sentiment with Keras
Build a word embedding model for Twitter sentiment analysis with TensorFlow 2.0 and Keras. Covers Tokenizer, Embedding layer, CNN, and binary classification.
Learn Natural Language Processing (NLP) concepts including Tokenization, Word Embeddings (Word2Vec, GloVe), Transformers, BERT, SpaCy, sentiment analysis, and sequence learning.
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Build a word embedding model for Twitter sentiment analysis with TensorFlow 2.0 and Keras. Covers Tokenizer, Embedding layer, CNN, and binary classification.
Classify SMS messages as spam or ham using TF-IDF and Word2Vec. Covers text preprocessing, feature extraction, and Naive Bayes and SVM model comparison.
Classify Amazon, IMDB, and Yelp reviews using spaCy's tokenization and scikit-learn. Build a machine learning pipeline to predict text sentiment.
Understand how spaCy's NLP processing pipeline works end to end. Covers tokenizer, tagger, named entity recognizer, and custom component registration.
Extract phone numbers, email addresses, and emojis from raw text using custom spaCy Matchers. Covers regex patterns, pipeline extensions, and span extraction.
Extract and match phrases from text using spaCy's Matcher and PhraseMatcher. Covers token-level rules, phrase patterns, and attribute-based text matching.
Extend spaCy's NLP pipeline with custom rules for named entity expansion. Covers EntityRuler, pattern matching, and combining ML models with rule-based logic.
Get started with spaCy for NLP tasks. Covers tokenization, POS tagging, named entity recognition, dependency parsing, and visualization using displaCy.
Learn to read, write, and process text, CSV, TSV, and PDF files in Python. Covers f-strings, file I/O operations, and Jupyter %%writefile for NLP workflows.
Build an extractive text summarizer with spaCy and NLTK. Covers word frequency scoring, sentence ranking, and summary generation without any ML model training.
Master end-to-end NLP text processing in Python. Covers Bag of Words, TF-IDF, Word2Vec, spaCy tokenization, and classification with machine learning.
Extract and process text from PDF files using PyPDF2 in Python. Covers PDF loading, page iteration, text extraction, and preparing output for NLP pipelines.