Introduction to Natural Language Processing (NLP)

• Overview of NLP and its applications
• Understanding the challenges and techniques in NLP
• Introduction to NLP libraries and tools in Python

Text Preprocessing and Tokenization

• Cleaning and preprocessing text data
• Tokenization and sentence segmentation
• Removing stop words and punctuation

Part-of-Speech (POS) Tagging

• Understanding POS tags and their significance
• POS tagging techniques and algorithms
• Using POS tagging libraries in Python

Named Entity Recognition (NER)

• Identifying and classifying named entities
• NER algorithms and approaches
• Implementing NER using Python libraries

Sentiment Analysis

• Analyzing sentiment from text data
• Sentiment analysis techniques (lexicon-based, machine learning)
• Sentiment analysis using Python libraries

Text Classification

• Understanding text classification tasks
• Feature extraction techniques (bag-of-words, TF-IDF)
• Building text classifiers using machine learning algorithms

Topic Modeling

• Extracting topics from text data
• Latent Dirichlet Allocation (LDA) and other topic modeling algorithms
• Topic modeling with Python libraries

Text Summarization

• Extractive and abstractive text summarization
• Summarization algorithms and approaches
• Text summarization using Python libraries

Word Embeddings and Word2Vec

• Understanding word embeddings and distributed representations
• Word2Vec algorithm and its variants
• Implementing Word2Vec models with Python libraries

Language Modeling and Text Generation

• Language modeling concepts and techniques
• Generating text using n-gram models and neural language models
• Text generation with Python libraries

Information Retrieval and Document Similarity

• Retrieving relevant information from text data
• Document similarity measures and techniques
• Building document similarity systems with Python

Text Mining and Feature Extraction

• Extracting meaningful information from text data
• Text mining techniques (frequency analysis, pattern matching)
• Feature extraction for text data using Python libraries

Deep Learning for NLP

• Introduction to deep learning models for NLP
• Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
• Applying deep learning to NLP tasks with Python frameworks

NLP Applications and Case Studies

• Exploring real-world NLP applications (chatbots, information extraction, etc.)
• Case studies and practical projects in NLP
• Ethical considerations and bias in NLP applications