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.
Learn Natural Language Processing (NLP) concepts including Tokenization, Word Embeddings (Word2Vec, GloVe), Transformers, BERT, SpaCy, sentiment analysis, and sequence learning.
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Master end-to-end NLP text processing in Python. Covers Bag of Words, TF-IDF, Word2Vec, spaCy tokenization, and classification with machine learning.
Build an extractive text summarizer with spaCy and NLTK. Covers word frequency scoring, sentence ranking, and summary generation without any ML model training.
Extract and process text from PDF files using PyPDF2 in Python. Covers PDF loading, page iteration, text extraction, and preparing output for NLP pipelines.
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.
Get started with spaCy for NLP tasks. Covers tokenization, POS tagging, named entity recognition, dependency parsing, and visualization using displaCy.
Extract and match phrases from text using spaCy's Matcher and PhraseMatcher. Covers token-level rules, phrase patterns, and attribute-based text matching.
Extract phone numbers, email addresses, and emojis from raw text using custom spaCy Matchers. Covers regex patterns, pipeline extensions, and span extraction.
Extend spaCy's NLP pipeline with custom rules for named entity expansion. Covers EntityRuler, pattern matching, and combining ML models with rule-based logic.
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.
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.
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Learn transformer architecture fundamentals and fine-tune LLMs with custom datasets.

Build NLP models using Python with Spacy, NLTK, and modern NLP techniques.