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Anshuman Mishra
1/1/2025
Published by Anshuman Mishra, 2025.
1/1/2025
PART 1: FOUNDATIONS OF NATURAL LANGUAGE PROCESSING
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PART 2: CLASSICAL NLP TECHNIQUES
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PART 3: MACHINE LEARNING FOR NLP
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PART 4: ADVANCED NLP WITH DEEP LEARNING
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PART 5: APPLICATIONS OF NLP
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PART 6: NLP PROJECTS AND FUTURE TRENDS
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Book Title:
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About the Book:
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Benefits of Studying This Book:
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1.1 Definition and Scope of NLP | Definition of NLP
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1.2 History and Evolution of NLP
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1.3 Applications in Real-World Scenarios
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2.1 Syntax, Semantics, and Pragmatics
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2.2 Morphology and Parts of Speech (POS)
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2.3 Sentence Structure and Parsing
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Text Processing and Preprocessing
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3.1 Tokenization and Sentence Segmentation
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3.2 Stopword Removal and Lemmatization
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3.3 Stemming and Word Normalization
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3.4 Handling Noisy and Unstructured Text
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4.1 Pattern Matching in Text
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4.2 Named Entity Recognition (NER)
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4.3 Using Regex in Python for NLP
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5.1 Bag of Words (BoW) Model
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5.2 Term Frequency-Inverse Document Frequency (TF-IDF)
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5.3 Word Embeddings and Vector Representations
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6.1 Rule-Based vs Statistical POS Tagging
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6.2 Dependency Parsing
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6.3 Constituency Parsing
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7.1 Supervised vs Unsupervised Learning in NLP
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7.3 Evaluation Metrics for NLP Models
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8.1 Introduction to Text Classification
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8.2 Naïve Bayes Classifier
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8.3 Logistic Regression for Text Classification
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8.4 Sentiment Analysis Applications
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9.1 Named Entity Recognition (NER)
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9.2 Relation Extraction and Coreference Resolution
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9.3 Applications of NER and Information Extraction
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10.1 Word Embeddings: Word2Vec, GloVe, and FastText | What are Word Embeddings?
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10.1.1 Word2Vec
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10.1.2 GloVe (Global Vectors for Word Representation)
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10.1.3 FastText
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10.2 Contextual Embeddings: ELMo and BERT
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10.2.1 ELMo (Embeddings from Language Models)
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10.2.2 BERT (Bidirectional Encoder Representations from Transformers)
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10.3 Applications of Word Embeddings and Contextual Representations
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11.1 Introduction to Sequence Modeling
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11.2 Understanding Recurrent Neural Networks (RNNs)
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11.3 LSTMs and GRUs for Text Processing
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What is Attention?
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Key Idea
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12.1.1 Types of Attention Mechanisms
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12.1.2 Self-Attention Mechanism
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12.2 The Transformer Architecture
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12.2.3 Transformer Implementation in Python
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12.3 BERT, GPT, and T5 for NLP | 12.3.1 BERT (Bidirectional Encoder Representations from Transformers)
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12.3.2 GPT (Generative Pre-trained Transformer)
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12.3.3 T5 (Text-to-Text Transfer Transformer)
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12.4 Applications of Transformers in NLP
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13.1 Sequence-to-Sequence Models
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13.2 Autoencoders for Text Generation | What is an Autoencoder?
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13.3 Extractive vs Abstractive Summarization
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13.4 Applications of Text Generation and Summarization
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14.1 Rule-Based vs AI-Powered Chatbots
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14.1.2 AI-Powered Chatbots
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14.2 Building a Simple Chatbot Using NLP
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14.2.1 Steps to Build an NLP Chatbot
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14.2.2 Chatbot with Machine Learning
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14.3 Case Studies: Siri, Alexa, and Google Assistant
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14.3.1 Siri (Apple)
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14.3.2 Alexa (Amazon)
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14.3.3 Google Assistant
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14.4 Applications of Chatbots and Conversational AI
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15.1 Basics of Speech Processing
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15.1.2 Key Concepts in Speech Processing
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15.2 Hidden Markov Models (HMM) for Speech Recognition
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15.3 Neural TTS Models
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15.4 Applications of Speech Recognition and TTS
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16.1 Classical Approaches vs Neural Machine Translation (NMT)
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16.1.2 Neural Machine Translation (NMT)
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16.2 Introduction to Google Translate and OpenNMT | 16.2.1 Google Translate: A Large-Scale NMT System
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16.2.2 OpenNMT: An Open-Source NMT Framework
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16.3 Applications of Machine Translation and Cross-Language NLP
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17.1 Building a Sentiment Analysis System
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17.2 Implementing a Text Summarization Tool
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17.3 Deploying NLP Models with Flask and FastAPI
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17.4 Applications of Real-World NLP Models
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18.1 Bias in Language Models | 18.1.1 What is Bias in NLP?
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18.1.3 How Bias is Introduced?
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18.1.4 Mitigating Bias in NLP Models
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18.2 Privacy and Security in NLP Applications
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18.3 Fairness and Ethical Use of NLP
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18.4 Case Studies of Ethical Concerns in NLP
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18.5 Future of Ethical NLP Development
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19.1 Multimodal NLP | 19.1.1 What is Multimodal NLP?
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19.2 Explainability in NLP Models | 19.2.1 Why is Explainability Important?
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19.2.3 Challenges in Explainable NLP
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19.3 Low-Resource Language Processing
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19.4 The Future of NLP
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