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Zero to Agentic Series' Articles

Back to Priyesh Dave's Series
Day 1: What Is NLP? A Practical Introduction for Engineers

Day 1: What Is NLP? A Practical Introduction for Engineers

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3 min read
Day 2: Text Preprocessing: Tokenization and Normalization

Day 2: Text Preprocessing: Tokenization and Normalization

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3 min read
Day 3: Stopwords, Stemming, and Lemmatization

Day 3: Stopwords, Stemming, and Lemmatization

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4 min read
Day 4: Bag-of-Words and Text Vectorization

Day 4: Bag-of-Words and Text Vectorization

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4 min read
Day 5: TF-IDF Explained and Implemented from Scratch

Day 5: TF-IDF Explained and Implemented from Scratch

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5 min read
Day 6: N-Grams and Statistical Language Modeling

Day 6: N-Grams and Statistical Language Modeling

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4 min read
Day 7: Part-of-Speech Tagging

Day 7: Part-of-Speech Tagging

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4 min read
Day 8: Named Entity Recognition (NER) Fundamentals

Day 8: Named Entity Recognition (NER) Fundamentals

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5 min read
Day 8: Named Entity Recognition (NER) Fundamentals

Day 8: Named Entity Recognition (NER) Fundamentals

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5 min read
Day 8: Named Entity Recognition (NER) Fundamentals

Day 8: Named Entity Recognition (NER) Fundamentals

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5 min read
Day 9: Text Classification with Naive Bayes and Logistic Regression

Day 9: Text Classification with Naive Bayes and Logistic Regression

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6 min read
Day 10: Evaluation Metrics for NLP: Precision, Recall, F1, Confusion Matrix

Day 10: Evaluation Metrics for NLP: Precision, Recall, F1, Confusion Matrix

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5 min read
Day 11: Why One-Hot Encoding Fails: The Case for Embeddings

Day 11: Why One-Hot Encoding Fails: The Case for Embeddings

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3 min read
Day 12: Word2Vec Explained: Skip-Gram and CBOW

Day 12: Word2Vec Explained: Skip-Gram and CBOW

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5 min read
Day 14: FastText and Subword Embeddings

Day 14: FastText and Subword Embeddings

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5 min read
Day 13: GloVe: Global Vectors for Word Representation

Day 13: GloVe: Global Vectors for Word Representation

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6 min read