ML Book 6 - Natural Language Processing with Python for Absolute Beginners

Table of Contents

1. Introduction

1.1 What is Natural Language Processing?

1.2 Why Python for NLP?

1.3 Applications of NLP

1.4 Challenges of NLP

1.5 How to Install Python and Required Libraries

2. Basic Text Processing

2.1 Reading and Writing Text Files

2.2 Tokenization

2.3 Stop Words Removal

2.4 Stemming and Lemmatization

2.5 Part-of-Speech Tagging

2.6 Named Entity Recognition

3. Text Representation and Feature Extraction

3.1 Bag-of-Words Model

3.2 Term Frequency-Inverse Document Frequency (TF-IDF) 3.3 Word Embeddings

3.4 Word2Vec and GloVe

3.5 Doc2Vec and FastText

4. Text Classification

4.1 What is Text Classification?

4.2 Types of Text Classification

4.3 Text Classification Workflow

4.4 Naive Bayes Classifier

4.5 Support Vector Machine Classifier

4.6 Logistic Regression Classifier

4.7 Evaluation Metrics for Text Classification

5. Sentiment Analysis

5.1 What is Sentiment Analysis?

5.2 Types of Sentiment Analysis

5.3 Sentiment Lexicons

5.4 Rule-Based Sentiment Analysis

5.5 Machine Learning-Based Sentiment Analysis

5.6 Deep Learning-Based Sentiment Analysis

6. Topic Modeling

6.1 What is Topic Modeling?

6.2 Types of Topic Modeling

6.3 Latent Dirichlet Allocation (LDA)

6.4 Non-Negative Matrix Factorization (NMF)

6.5 Evaluation Metrics for Topic Modeling

7. Text Summarization

7.1 What is Text Summarization?

7.2 Types of Text Summarization

7.3 Extractive Text Summarization

7.4 Abstractive Text Summarization

7.5 Evaluation Metrics for Text Summarization

8. Text Generation

8.1 What is Text Generation?

8.2 Types of Text Generation

8.3 Markov Chain Text Generation

8.4 Recurrent Neural Network (RNN) Text Generation 8.5 Transformer Text Generation

9. Conclusion

9.1 Summary of the Book

9.2 Future Trends and Directions of NLP

9.3 Resources and References for Further Learning