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Natural Language Processing with Python Updated Edition

01Introduction to NLP0/5

11.1 What is Natural Language Processing (NLP)?21.2 Significance and Applications of NLP 31.3 Overview of Python for NLP 5Chapter 1 Summary 4Practical Exercises - Chapter 1: Introduction to NLP

02Basic Text Processing0/6

12.1 Understanding Text Data 22.2 Text Cleaning: Stop Word Removal, Stemming, Lemmatization 32.3 Regular Expressions 42.4 Tokenization 6Chapter 2 Summary 5Practical Exercises

03Feature Engineering for NLP0/6

13.1 Bag of Words 23.2 TF-IDF 33.3 Word Embeddings (Word2Vec, GloVe)43.4 Introduction to BERT Embeddings 6Chapter Summary 5Practical Exercises

04Quiz Part I: Foundations of NLP0/8

7Advanced Understanding 8Answers 1Chapter 1: Introduction to NLP 2Chapter 2: Basic Text Processing 3Chapter 3: Feature Engineering for NLP 5Code Implementation 6Conceptual Understanding 4Practical Applications

05Language Modeling0/6

14.1 N-grams 24.2 Hidden Markov Models 34.3 Recurrent Neural Networks (RNNs)44.4 Long Short-Term Memory Networks (LSTMs)6Chapter Summary 5Practical Exercises

06Syntax and Parsing0/5

15.1 Parts of Speech (POS) Tagging 25.2 Named Entity Recognition (NER)35.3 Dependency Parsing 5Chapter Summary 4Practical Exercises

07Sentiment Analysis0/5

16.1 Rule-Based Approaches 26.2 Machine Learning Approaches 36.3 Deep Learning Approaches 5Chapter Summary 4Practical Exercises

08Quiz Part II: Advanced Text Processing and Modeling0/4

4Answers 1Chapter 4: Language Modeling 2Chapter 5: Syntax and Parsing 3Chapter 6: Sentiment Analysis

09Topic Modeling0/5

17.1 Latent Semantic Analysis (LSA)27.2 Latent Dirichlet Allocation (LDA)37.3 Hierarchical Dirichlet Process (HDP)5Chapter Summary 4Practical Exercises

10Text Summarization0/4

18.1 Extractive Summarization 28.2 Abstractive Summarization 4Chapter Summary 3Practical Exercises

11Quiz Part III: Topic Modeling and Text Summarization0/3

3Answers 1Chapter 7: Topic Modeling 2Chapter 8: Text Summarization

12Machine Translation0/5

19.1 Sequence to Sequence Models 29.2 Attention Mechanisms 39.3 Transformer Models 5Chapter Summary 4Practical Exercises

13Introduction to Chatbots0/5

110.1 What is a Chatbot?210.2 Applications of Chatbots 310.3 Types of Chatbots: Rule-Based, Self-Learning, and Hybrid 5Chapter Summary 4Practical Exercises

14Chatbot Project: Personal Assistant Chatbot0/6

111.1 Project Introduction and Design 211.2 Data Collection and Preprocessing 311.3 Building and Training the Chatbot 411.4 Evaluating and Deploying the Chatbot 511.5 Improving and Maintaining the Chatbot 6Chapter Summary

15Project: News Aggregator0/6

112.1 Project Introduction and Design 212.2 Data Collection and Preprocessing 312.3 Implementing Text Summarization and Topic Modeling 412.4 Building the User Interface 512.5 Evaluating and Deploying the Aggregator 6Chapter Summary

16Project: Sentiment Analysis Dashboard0/6

113.1 Project Introduction and Design 213.2 Data Collection and Preprocessing 313.3 Building and Training Sentiment Analysis Models 413.4 Developing the Dashboard Interface 513.5 Evaluating and Deploying the Dashboard 6Chapter Summary

17Quiz Part IV: Applications and Advanced Techniques0/7

7Answer Key 6Bonus Question 2Chapter 10: Introduction to Chatbots 3Chapter 11: Chatbot Project: Personal Assistant Chatbot 4Chapter 12: Project: News Aggregator 5Chapter 13: Project: Sentiment Analysis Dashboard 1Chapter 9: Machine Translation

Natural Language Processing with Python Updated EditionChapter 177

Answer Key

Section 1 of 7-~ 12 min read-Synced from Cuantum content

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