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Natural Language Processing(NLP)

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  Step 1: Input Text Collect raw text data from sources such as documents, emails, social media, or user input. Step 2: Text Preprocessing Clean and prepare the text for analysis. Convert text to lowercase Remove punctuation and special characters Remove stop words (e.g., is, the, and ) Tokenization (split text into words or sentences) Stemming or Lemmatization (reduce words to root form) Step 3: Feature Extraction Convert text into numerical representations. Bag of Words (BoW) Term Frequency–Inverse Document Frequency (TF-IDF) Word Embeddings (Word2Vec, GloVe) Step 4: Model Selection Choose an appropriate NLP model based on the task. Naïve Bayes Support Vector Machine (SVM) Recurrent Neural Networks (RNN) Transformer models Step 5: Model Training Train the selected model using labeled or unlabeled data. Step 6: NLP Task Execution Perform specific NLP tasks such as: Text Classification Sentiment Analysis Named Entity Recognition (NER) Mac...
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Agile and Devops

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Natural Language Processing: Teaching Machines to Understand Human Language Natural Language Processing (NLP) is a fascinating branch of Artificial Intelligence (AI) that focuses on enabling computers to understand, interpret, and respond to human language in a meaningful way. From voice assistants like Siri and Alexa to chatbots, translation tools, and spam filters, NLP plays a vital role in many technologies we use every day. At its core, NLP bridges the gap between human communication and machine understanding. Human language is complex, filled with ambiguity, context, slang, and emotions. NLP combines linguistics, computer science, and machine learning to process both written and spoken language, allowing systems to analyze text, extract meaning, and generate responses that feel natural. One of the key components of NLP is text preprocessing, which includes tasks such as tokenization, stop-word removal, and stemming or lemmatization. These steps help convert raw text into a structu...
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