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NLP by Vinod

A structured public journey from NLP fundamentals to real-world AI systems.

Vinod Codes is where I document my learning in AI, Machine Learning, Deep Learning, Natural Language Processing, Generative AI, and practical projects.

The main series here is NLP by Vinod — a learner-builder journey where I explain concepts with intuition, Python examples, mistakes, GitHub work, and honest implementation notes.

Start here: follow the Foundations Track first, then move into deep learning, transformers, projects, and real-world NLP systems.
NLP Foundations Python for NLP Machine Learning Deep Learning Real Projects
Start with the NLP Roadmap View GitHub Repository

Featured

PyTorch for Deep Learning - From Tensors to ANN Training

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NLP by Vinod - Deep Learning PyTorch Foundations PyTorch for Deep Learning - From Tensors to ANN Training. After NLP libraries, I moved into deep learning foundations and learned how PyTorch helps build neural networks using tensors, autograd, training loops, DataLoader, GPU training and hyperparameter tuning. PyTorch Tensors Autograd ANN
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NLP Libraries - NLTK, spaCy, TextBlob and Stanza in Practice

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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
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Word Embeddings in NLP - Moving Beyond Sparse Features

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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
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Feature Extraction in NLP - From Clean Text to Count-Based Features

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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
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Text Preprocessing in NLP - Cleaning Raw Text Before Feature Extraction

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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
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Data Acquisition for NLP - Collecting Text Before Preprocessing

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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
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