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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
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Aug 24
Day 1: What Is NLP? A Practical Introduction for Engineers
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3 min read
Day 2: Text Preprocessing: Tokenization and Normalization
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Aug 25
Day 2: Text Preprocessing: Tokenization and Normalization
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3 min read
Day 3: Stopwords, Stemming, and Lemmatization
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Aug 26
Day 3: Stopwords, Stemming, and Lemmatization
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4 min read
Day 4: Bag-of-Words and Text Vectorization
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Aug 27
Day 4: Bag-of-Words and Text Vectorization
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4 min read
Day 5: TF-IDF Explained and Implemented from Scratch
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Aug 29
Day 5: TF-IDF Explained and Implemented from Scratch
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5 min read
Day 6: N-Grams and Statistical Language Modeling
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Aug 30
Day 6: N-Grams and Statistical Language Modeling
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4 min read
Day 7: Part-of-Speech Tagging
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Aug 31
Day 7: Part-of-Speech Tagging
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4 min read
Day 8: Named Entity Recognition (NER) Fundamentals
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Sep 1
Day 8: Named Entity Recognition (NER) Fundamentals
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5 min read
Day 8: Named Entity Recognition (NER) Fundamentals
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Sep 1
Day 8: Named Entity Recognition (NER) Fundamentals
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5 min read
Day 8: Named Entity Recognition (NER) Fundamentals
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Sep 1
Day 8: Named Entity Recognition (NER) Fundamentals
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5 min read
Day 9: Text Classification with Naive Bayes and Logistic Regression
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Sep 2
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
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Sep 3
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
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Sep 4
Day 11: Why One-Hot Encoding Fails: The Case for Embeddings
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Day 12: Word2Vec Explained: Skip-Gram and CBOW
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Sep 6
Day 12: Word2Vec Explained: Skip-Gram and CBOW
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Day 14: FastText and Subword Embeddings
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Sep 9
Day 14: FastText and Subword Embeddings
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5 min read
Day 13: GloVe: Global Vectors for Word Representation
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Sep 9
Day 13: GloVe: Global Vectors for Word Representation
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6 min read
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