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Mastering Natural Language Processing Beginner To Advanced Level Guide(1st Edition)

Authors:

Husn Ara

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ISBN: B0DK9LM8Y9, 979-8343611366

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Book Summary: Mastering Natural Language Processing: A Comprehensive Guide From Beginner To AdvancedMastering Natural Language Processing Is A Must-read Book Designed For Anyone Looking To Gain In-depth Knowledge Of Natural Language Processing (NLP). This Guide Takes You On A Journey From Beginner-level Concepts To Advanced Techniques, Making It Suitable For Both Novices And Experienced Professionals In The Field Of Data Science And Machine Learning. NLP Has Become One Of The Most Critical Domains In Artificial Intelligence, Powering Applications From Chatbots To Sentiment Analysis, Machine Translation, And Beyond. This Book Is Structured To Give A Solid Foundation And Practical Hands-on Experience In NLP.What Is Natural Language Processing (NLP)?The Book Begins By Introducing The Core Concept Of NLP, Explaining How Computers Can Process, Understand, And Generate Human Language. It Dives Into The Basic Building Blocks, Such As Tokenization, Stemming, Lemmatization, And Explains Why NLP Is A Vital Field In Today's AI-driven World.Linguistic Fundamentals And Text ProcessingTo Master NLP, Understanding Language Structure Is Essential. The Book Covers Key Linguistic Concepts Such As Syntax, Semantics, And Pragmatics. It Provides Code Examples For Text Preprocessing Techniques Like Removing Stopwords, Handling Out-of-vocabulary (OOV) Words, And Normalizing Text, Ensuring You Can Clean And Prepare Text Data Efficiently.Supervised And Unsupervised Learning Techniques For NLPDelve Into Machine Learning Approaches To NLP With Hands-on Tutorials On Classification Algorithms Such As Logistic Regression, Support Vector Machines (SVM), And Decision Trees. You’ll Also Explore Unsupervised Learning Methods, Including Clustering Techniques Like K-Means, DBSCAN, And Topic Modeling Using Latent Dirichlet Allocation (LDA).Advanced Embedding Techniques: Word2Vec, GloVe, And FastTextLearn About Word Embeddings, A Crucial Component Of Modern NLP, Which Helps Capture The Meaning Of Words In A High-dimensional Space. The Book Covers Advanced Models Like Word2Vec (Continuous Bag Of Words And Skip-Gram), GloVe, And FastText, Complete With Practical Examples To Help You Implement These Embeddings In Your Projects.Deep Learning For NLP: RNN, LSTM, GRU, And Transformer ModelsAs You Progress, The Book Introduces Deep Learning Techniques Like Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), And Gated Recurrent Units (GRU), With Detailed Code Implementations. You’ll Also Explore The Revolutionary Transformer Architecture And Its Attention Mechanism, Which Has Transformed NLP With Models Like BERT And GPT.Natural Language Generation And Machine TranslationThe Book Covers Natural Language Generation (NLG) With Detailed Examples And Code For Generating Human-like Text Using Models Like GPT. It Also Dives Into Machine Translation, Discussing Both Rule-based And Statistical Approaches, Followed By Advanced Neural Machine Translation Techniques Using Sequence-to-sequence (Seq2Seq) Models.Named Entity Recognition (NER) And Sentiment AnalysisUnderstand How To Extract Meaningful Entities Like Names, Dates, And Locations From Text Using Named Entity Recognition (NER). The Book Also Covers Sentiment Analysis, Showing You How To Classify Emotions From Text Using Lexicon-based Approaches And Machine Learning Models.NLP For Social Media And Multimodal LearningSocial Media Has Its Own Set Of Challenges For NLP, With Noisy And Informal Text Data. This Section Helps You Apply NLP Techniques To Social Media Platforms, Handling Tasks Such As Sentiment Mining, Topic Modeling, And Text Classification. Additionally, The Book Covers Multimodal Learning, Integrating Text With Other Data Types Like Images And Audio.