NLP
Learn Natural Language Processing (NLP) concepts including Tokenization, Word Embeddings (Word2Vec, GloVe), Transformers, BERT, SpaCy, sentiment analysis, and sequence learning.
NLP Tutorials (12)
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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.
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Sentiment Classification with spaCy
Classify Amazon, IMDB, and Yelp reviews using spaCy's tokenization and scikit-learn. Build a machine learning pipeline to predict text sentiment.
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Processing Pipeline in SpaCy
Understand how spaCy's NLP processing pipeline works end to end. Covers tokenizer, tagger, named entity recognizer, and custom component registration.
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Phone, Email & Emoji Extraction with spaCy
Extract phone numbers, email addresses, and emojis from raw text using custom spaCy Matchers. Covers regex patterns, pipeline extensions, and span extraction.
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Rule-Based Text Extraction and Matching with spaCy
Extract and match phrases from text using spaCy's Matcher and PhraseMatcher. Covers token-level rules, phrase patterns, and attribute-based text matching.
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Working with Text Files in Python for NLP
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.
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Text Summarization using NLP
Build an extractive text summarizer with spaCy and NLTK. Covers word frequency scoring, sentence ranking, and summary generation without any ML model training.
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NLP: End to End Text Processing for Beginners
Master end-to-end NLP text processing in Python. Covers Bag of Words, TF-IDF, Word2Vec, spaCy tokenization, and classification with machine learning.
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Extract Text from PDF Files in Python for NLP
Extract and process text from PDF files using PyPDF2 in Python. Covers PDF loading, page iteration, text extraction, and preparing output for NLP pipelines.
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Combining NLP Models and Custom Rules in spaCy
Extend spaCy's NLP pipeline with custom rules for named entity expansion. Covers EntityRuler, pattern matching, and combining ML models with rule-based logic.
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spaCy Introduction: Linguistic Feature Extraction
Get started with spaCy for NLP tasks. Covers tokenization, POS tagging, named entity recognition, dependency parsing, and visualization using displaCy.
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Spam Text Message Classification with NLP
Classify SMS messages as spam or ham using TF-IDF and Word2Vec. Covers text preprocessing, feature extraction, and Naive Bayes and SVM model comparison.
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