Chapter 1: Introduction to Natural Language Processing
- Lesson 1: What is NLP?
- Lesson 2: The History of NLP
- Lesson 3: Applications of NLP
- Lesson 4: Challenges in NLP
- Lesson 5: Overview of the Course
Chapter 2: Basics of Linguistics for NLP
- Lesson 1: Words, Sentences, and Syntax
- Lesson 2: Morphology and Lemmatization
- Lesson 3: Part-of-Speech (POS) Tagging
- Lesson 4: Parsing and Constituency
- Lesson 5: Semantics and Pragmatics
Chapter 3: Text Preprocessing
- Lesson 1: Tokenization Techniques
- Lesson 2: Stop Words Removal
- Lesson 3: Stemming and Lemmatization
- Lesson 4: Lowercasing and Normalization
- Lesson 5: Handling Special Characters and Emojis
Chapter 4: Basic Feature Engineering
- Lesson 1: Bag of Words (BoW) Model
- Lesson 2: N-grams and Their Applications
- Lesson 3: Term Frequency and Inverse Document Frequency (TF-IDF)
- Lesson 4: Word Frequency Analysis
- Lesson 5: One-Hot Encoding
Chapter 5: Sentiment Analysis
- Lesson 1: What is Sentiment Analysis?
- Lesson 2: Applications of Sentiment Analysis
- Lesson 3: Rule-Based Sentiment Analysis
- Lesson 4: Sentiment Analysis Using Machine Learning
- Lesson 5: Evaluating Sentiment Models
Chapter 6: Text Classification
- Lesson 1: Basics of Text Classification
- Lesson 2: Common Text Classification Algorithms
- Lesson 3: Building a Simple Text Classifier
- Lesson 4: Applications of Text Classification
- Lesson 5: Evaluation Metrics for Text Classification
Chapter 7: Named Entity Recognition (NER)
- Lesson 1: Introduction to NER
- Lesson 2: Common Entity Types
- Lesson 3: Rule-Based NER Systems
- Lesson 4: Statistical NER Models
- Lesson 5: Applications of NER
Chapter 8: Language Modeling Basics
- Lesson 1: What is a Language Model?
- Lesson 2: Unigram and Bigram Models
- Lesson 3: Perplexity in Language Modeling
- Lesson 4: Smoothing Techniques
- Lesson 5: Limitations of Basic Language Models
Chapter 9: Word Embeddings
- Lesson 1: Introduction to Word Embeddings
- Lesson 2: Word2Vec (Skip-Gram and CBOW)
- Lesson 3: GloVe Embeddings
- Lesson 4: Evaluating Word Embeddings
- Lesson 5: Visualizing Word Embeddings
Chapter 10: Introduction to Machine Translation
- Lesson 1: History of Machine Translation
- Lesson 2: Rule-Based Machine Translation
- Lesson 3: Statistical Machine Translation (SMT)
- Lesson 4: Phrase-Based Translation Models
- Lesson 5: Challenges in Machine Translation
Chapter 11: Tools and Libraries for NLP
- Lesson 1: Overview of NLP Tools
- Lesson 2: Installing NLTK
- Lesson 3: Basic Functions in spaCy
- Lesson 4: Gensim for Topic Modeling
- Lesson 5: TextBlob for Quick NLP Tasks
Chapter 12: Evaluation Metrics in NLP
- Lesson 1: Precision, Recall, and F1 Score
- Lesson 2: BLEU Score for Translation
- Lesson 3: ROUGE for Summarization
- Lesson 4: Confusion Matrix in NLP Tasks
- Lesson 5: Cross-Validation in NLP
Chapter 1: Transformer Networks
- Lesson 1: What are Transformer Networks?
- Lesson 2: Self-Attention Mechanism
- Lesson 3: Multi-Head Attention
- Lesson 4: Encoder-Decoder Architecture
- Lesson 5: Applications of Transformers
Chapter 2: BERT and its Variants
- Lesson 1: Introduction to BERT
- Lesson 2: Pretraining and Fine-Tuning BERT
- Lesson 3: Variants like RoBERTa and DistilBERT
- Lesson 4: Using BERT for Text Classification
- Lesson 5: Case Studies of BERT Applications
Chapter 3: Latent Dirichlet Allocation (LDA)
- Lesson 1: Introduction to Topic Modeling
- Lesson 2: Basics of LDA
- Lesson 3: Training an LDA Model
- Lesson 4: Applications of LDA in NLP
- Lesson 5: Comparing LDA with Other Models
Chapter 4: Convolutional Neural Networks (CNNs) in NLP
- Lesson 1: Overview of CNNs
- Lesson 2: Adapting CNNs for Text Data
- Lesson 3: Case Study: Text Classification with CNNs
- Lesson 4: CNNs vs RNNs in NLP
- Lesson 5: Limitations of CNNs for NLP
Chapter 5: Sequence-to-Sequence Models
- Lesson 1: Introduction to Seq2Seq Models
- Lesson 2: Attention Mechanism in Seq2Seq
- Lesson 3: Applications: Chatbots and Summarization
- Lesson 4: Building a Simple Seq2Seq Model
- Lesson 5: Evaluating Seq2Seq Models
Chapter 6: Transfer Learning in NLP
- Lesson 1: Introduction to Transfer Learning
- Lesson 2: Pretrained Models for NLP
- Lesson 3: Fine-Tuning for Specific Tasks
- Lesson 4: Comparing Transfer Learning Approaches
- Lesson 5: Future Trends in Transfer Learning
Chapter 7: Ethics in NLP
- Lesson 1: Bias in Language Models
- Lesson 2: Privacy Concerns in NLP Applications
- Lesson 3: Fairness in NLP Systems
- Lesson 4: Case Studies on Ethical Issues
- Lesson 5: Guidelines for Ethical NLP Development
Chapter 8: Advanced Machine Translation
- Lesson 1: Neural Machine Translation (NMT)
- Lesson 2: Transformers for Machine Translation
- Lesson 3: Challenges in Multilingual NLP
- Lesson 4: Real-World Applications of Translation Models
- Lesson 5: Evaluating NMT Systems
Chapter 9: Summarization Techniques
- Lesson 1: Extractive vs Abstractive Summarization
- Lesson 2: Building Extractive Summarizers
- Lesson 3: Abstractive Summarization Using Transformers
- Lesson 4: Applications of Summarization in Business
- Lesson 5: Evaluating Summarization Models
Chapter 10: Advanced Sentiment Analysis
- Lesson 1: Sentiment Analysis Using Deep Learning
- Lesson 2: Aspect-Based Sentiment Analysis
- Lesson 3: Emotion Detection Models
- Lesson 4: Challenges in Sentiment Analysis
- Lesson 5: Advanced Case Studies
Chapter 11: Emerging Topics in NLP
- Lesson 1: Large Language Models (e.g., GPT)
- Lesson 2: Multimodal NLP
- Lesson 3: Cross-Lingual NLP Systems
- Lesson 4: Low-Resource Language Processing
- Lesson 5: NLP for Healthcare
Chapter 12: Case Studies and Final Project
- Lesson 1: Real-World NLP Case Studies
- Lesson 2: End-to-End Project: Text Classification
- Lesson 3: End-to-End Project: Chatbot Development
- Lesson 4: Project Evaluation and Feedback
- Lesson 5: Future Trends in NLP
Chapter 13: Mastering NLTK Library for Natural Language Processing
- Lesson 1: Introduction to NLTK
- Lesson 2: Text Preprocessing with NLTK
- Lesson 3: Exploring NLTK's Corpus and Resources
- Lesson 4: Part-of-Speech Tagging with NLTK
- Lesson 5: Named Entity Recognition (NER) with NLTK
- Lesson 6: Syntactic Parsing with NLTK
- Lesson 7: Sentiment Analysis with NLTK
- Lesson 8: Text Classification with NLTK
- Lesson 9: WordNet Integration in NLTK
- Lesson 10: Advanced NLP Tasks with NLTK
Chapter 14: spaCy Library, Advanced Usage and Pipeline Customization
- Lesson 1: Introduction to spaCy Pipelines: An In-Depth Look
- Lesson 2: Adding and Removing Components in spaCy Pipelines
- Lesson 3: Creating Custom Pipeline Components
- Lesson 4: Fine-Tuning spaCy's Built-in Models
- Lesson 5: Customizing Tokenizer Behavior in spaCy
- Lesson 6: Training Custom Named Entity Recognizers in spaCy
- Lesson 7: Integrating External Models with spaCy Pipelines
- Lesson 8: Using spaCy’s Matcher and PhraseMatcher Effectively
- Lesson 9: Efficient Data Management with spaCy’s Doc and Span Objects
- Lesson 10: Exporting and Sharing Customized spaCy Pipelines
Chapter 15: CoreNLP Library, Features and Integration with Python
- Lesson 1: Introduction to CoreNLP
- Lesson 2: CoreNLP Features and Capabilities
- Lesson 3: Installing and Setting Up CoreNLP
- Lesson 4: CoreNLP Architecture and Processing Pipeline
- Lesson 5: Integrating CoreNLP with Python Using StanfordNLP or PyCoreNLP
- Lesson 6: CoreNLP Text Analysis: Practical Examples
- Lesson 7: Using CoreNLP Models and Customization
- Lesson 8: CoreNLP Server: Setting Up and Using the REST API
- Lesson 9: Optimizing CoreNLP for Large-Scale Text Processing
- Lesson 10: Comparing CoreNLP with Other NLP Libraries
Chapter 16: Gensim Library, Deep Dive into Topic Modeling
- Lesson 1: Introduction to Gensim and Topic Modeling
- Lesson 2: Installing and Setting Up Gensim
- Lesson 3: Preparing Text Data for Topic Modeling
- Lesson 4: Understanding Latent Semantic Analysis (LSA) with Gensim
- Lesson 5: Exploring Latent Dirichlet Allocation (LDA) with Gensim
- Lesson 6: Tuning Hyperparameters for LDA
- Lesson 7: Visualizing Topic Models with pyLDAvis and Wordclouds
- Lesson 8: Advanced Topic Modeling Techniques in Gensim
- Lesson 9: Applications of Gensim for Real-World Use Cases
- Lesson 10: Best Practices and Troubleshooting in Gensim
Chapter 17: Pattern Library, Web Mining and NLP Features
- Lesson 1: Introduction to Pattern Library for Web Mining and NLP
- Lesson 2: Setting Up Pattern: Installation and Environment Configuration
- Lesson 3: Web Scraping with Pattern: Fetching and Parsing HTML Content
- Lesson 4: Text Processing Basics in Pattern: Tokenization, Stemming, and Lemmatization
- Lesson 5: Pattern's Sentiment Analysis: Measuring Emotional Tone in Text
- Lesson 6: Language Translation and Detection with Pattern
- Lesson 7: Working with Pattern's WordNet Integration for Semantic Analysis
- Lesson 8: Building Basic Web Mining Applications Using Pattern
- Lesson 9: NLP Feature Engineering with Pattern: Practical Use Cases
- Lesson 10: Advanced Topics in Pattern: Customizing and Extending Functionalities
Chapter 18: TextBlob Library, Sentiment Analysis and Quick NLP Tasks
- Lesson 1: Introduction to TextBlob
- Lesson 2: Installing and Setting Up TextBlob
- Lesson 3: Basic Text Processing with TextBlob
- Lesson 4: Sentiment Analysis with TextBlob
- Lesson 5: TextBlob for Spelling Correction
- Lesson 6: Language Detection and Translation
- Lesson 7: Text Classification with TextBlob
- Lesson 8: Working with N-grams and Word Frequencies
- Lesson 9: Customizing TextBlob with Extensions
- Lesson 10: Real-World Applications of TextBlob
Chapter 19: AllenNLP Library, Advanced NLP Models and Case Studies
- Lesson 1: Introduction to AllenNLP: Features and Ecosystem
- Lesson 2: Installing and Setting Up AllenNLP
- Lesson 3: Understanding AllenNLP's Model Architecture
- Lesson 4: Building Custom NLP Models with AllenNLP
- Lesson 5: Dataset Management and Preprocessing in AllenNLP
- Lesson 6: Training and Fine-Tuning NLP Models with AllenNLP
- Lesson 7: Using AllenNLP for Named Entity Recognition (NER)
- Lesson 8: AllenNLP in Practice: Sentiment Analysis Case Study
- Lesson 9: Interpreting and Visualizing Model Results in AllenNLP
- Lesson 10: Deploying AllenNLP Models in Production
Chapter 20: BERTopic Library, Dynamic Topic Modeling
- Lesson 1: Introduction to BERTopic and Dynamic Topic Modeling
- Lesson 2: Installing and Setting Up BERTopic
- Lesson 3: Understanding the Core Concepts of BERTopic
- Lesson 4: Generating Topics with BERTopic
- Lesson 5: Dynamic Topic Modeling with BERTopic
- Lesson 6: Visualizing Topics with BERTopic
- Lesson 7: Advanced Customization in BERTopic
- Lesson 8: Evaluating Topic Models with BERTopic
- Lesson 9: BERTopic Integration with Other NLP Libraries
- Lesson 10: Real-World Applications of BERTopic
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