Vinod Codes | AI Engineering & Data Science

NLP by Vinod - Foundations NLP Libraries NLP Libraries - NLTK, spaCy, TextBlob and Stanza in Practice. After embeddings, I explored the practical NLP libraries that help with tokenization, POS tagging, NER, sentiment analysis, parsing, multilingual processing and quick NLP experiments. NLTK spaCy TextBlob Stanza

NLP by Vinod - Foundations Text Representation Word Embeddings in NLP - Moving Beyond Sparse Features. After count-based feature extraction, I learned why NLP needs dense vectors that can capture similarity, meaning and context better than Bag of Words and TF-IDF. NLP Embeddings Word2Vec GloVe

NLP by Vinod - Foundations Feature Extraction Feature Extraction in NLP - From Clean Text to Count-Based Features. After text preprocessing, I learned how cleaned words are converted into numbers using word count, frequency distribution, one-hot encoding, Bag of Words, n-grams and TF-IDF. NLP Feature Extraction Bag of Words TF-IDF

NLP by Vinod - Foundations Text Preprocessing Text Preprocessing in NLP - Cleaning Raw Text Before Feature Extraction. After data acquisition, I learned how raw text is cleaned, normalized, tokenized, and prepared before feature extraction. This post connects basic and advanced preprocessing from my notebooks into one clear sequence. NLP Text Cleaning Tokenization Stemming

Data Acquisition for NLP - Collecting Text Before Preprocessing NLP by Vinod - Foundations Data Acquisition Data Acquisition for NLP - Collecting Text Before Preprocessing. Data acquisition is the first real step before text preprocessing. In this post, I am documenting how I collected data using web scraping, JSON files, SQL, APIs, CSV workflows, and basic EDA. NLP Data Acquisition Web Scraping APIs

Python Strings & Regex for NLP - The Real Foundation NLP by Vinod - Foundations NLP Core Skills Python Strings & Regex for NLP - The Real Foundation. Before tokenization, embeddings, transformers, or BERT, every NLP pipeline starts with raw text. This post is my practical breakdown of Python strings, Unicode, regex patterns, and text cleaning for NLP. NLP Python Regex Text Preprocessing