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Natural Language Processing with Python Updated Edition

17 chapters and 92 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
17
Reading time
~ 16h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

17 chapters - 92 sections

Chapter 1: Introduction to NLP

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1.1 What is Natural Language Processing (NLP)?16m1.2 Significance and Applications of NLP24m1.3 Overview of Python for NLP27mPractical Exercises - Chapter 1: Introduction to NLP4mChapter 1 Summary4m

Chapter 2: Basic Text Processing

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2.1 Understanding Text Data32m2.2 Text Cleaning: Stop Word Removal, Stemming, Lemmatization23m2.3 Regular Expressions26m2.4 Tokenization25mPractical Exercises5mChapter 2 Summary4m

Chapter 3: Feature Engineering for NLP

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3.1 Bag of Words17m3.2 TF-IDF21m3.3 Word Embeddings (Word2Vec, GloVe)20m3.4 Introduction to BERT Embeddings22mPractical Exercises6mChapter Summary4m

Quiz Part I: Foundations of NLP

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Chapter 1: Introduction to NLP1mChapter 2: Basic Text Processing1mChapter 3: Feature Engineering for NLP1mPractical Applications1mCode Implementation1mConceptual Understanding1mAdvanced Understanding1mAnswers2m

Chapter 4: Language Modeling

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4.1 N-grams22m4.2 Hidden Markov Models24m4.3 Recurrent Neural Networks (RNNs)20m4.4 Long Short-Term Memory Networks (LSTMs)16mPractical Exercises8mChapter Summary4m

Chapter 5: Syntax and Parsing

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5.1 Parts of Speech (POS) Tagging20m5.2 Named Entity Recognition (NER)18m5.3 Dependency Parsing18mPractical Exercises6mChapter Summary3m

Chapter 6: Sentiment Analysis

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6.1 Rule-Based Approaches18m6.2 Machine Learning Approaches24m6.3 Deep Learning Approaches26mPractical Exercises7mChapter Summary4m

Quiz Part II: Advanced Text Processing and Modeling

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Chapter 4: Language Modeling1mChapter 5: Syntax and Parsing1mChapter 6: Sentiment Analysis1mAnswers1m

Chapter 7: Topic Modeling

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7.1 Latent Semantic Analysis (LSA)13m7.2 Latent Dirichlet Allocation (LDA)21m7.3 Hierarchical Dirichlet Process (HDP)26mPractical Exercises5mChapter Summary4m

Chapter 8: Text Summarization

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8.1 Extractive Summarization33m8.2 Abstractive Summarization18mPractical Exercises7mChapter Summary4m

Quiz Part III: Topic Modeling and Text Summarization

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Chapter 7: Topic Modeling2mChapter 8: Text Summarization2mAnswers1m

Chapter 9: Machine Translation

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9.1 Sequence to Sequence Models19m9.2 Attention Mechanisms24m9.3 Transformer Models21mPractical Exercises11mChapter Summary4m

Chapter 10: Introduction to Chatbots

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10.1 What is a Chatbot?14m10.2 Applications of Chatbots7m10.3 Types of Chatbots: Rule-Based, Self-Learning, and Hybrid20mPractical Exercises6mChapter Summary4m

Chapter 11: Chatbot Project: Personal Assistant Chatbot

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11.1 Project Introduction and Design5m11.2 Data Collection and Preprocessing10m11.3 Building and Training the Chatbot7m11.4 Evaluating and Deploying the Chatbot11m11.5 Improving and Maintaining the Chatbot11mChapter Summary4m

Chapter 12: Project: News Aggregator

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12.1 Project Introduction and Design5m12.2 Data Collection and Preprocessing10m12.3 Implementing Text Summarization and Topic Modeling6m12.4 Building the User Interface11m12.5 Evaluating and Deploying the Aggregator10mChapter Summary4m

Chapter 13: Project: Sentiment Analysis Dashboard

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13.1 Project Introduction and Design6m13.2 Data Collection and Preprocessing7m13.3 Building and Training Sentiment Analysis Models8m13.4 Developing the Dashboard Interface11m13.5 Evaluating and Deploying the Dashboard9mChapter Summary4m

Quiz Part IV: Applications and Advanced Techniques

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Chapter 9: Machine Translation1mChapter 10: Introduction to Chatbots1mChapter 11: Chatbot Project: Personal Assistant Chatbot1mChapter 12: Project: News Aggregator1mChapter 13: Project: Sentiment Analysis Dashboard1mBonus Question1mAnswer Key1m