Prerequisites

• Python programming skills
• Basic machine learning understanding
• Linear algebra fundamentals
• Basic statistics knowledge
• Recommended: Basic text processing experience
• Computer with minimum 8GB RAM

12-Week Natural Language Processing Course Syllabus

Week 1: NLP Foundations

• Introduction to NLP
• Text data characteristics
• NLP applications overview
• Language processing challenges
• Computational linguistics basics
• Text representation techniques
• Python NLP library introduction

Week 2: Text Preprocessing

• Tokenization techniques
• Stopword removal
• Stemming and lemmatization
• Regular expressions
• Text normalization
• Cleaning unstructured text
• Preprocessing pipeline development

Week 3: Language Modeling

• Bag-of-Words model
• TF-IDF techniques
• Word embedding strategies
• Word2Vec and GloVe
• N-gram language models
• Distributional semantics
• Vector representation methods

Week 4: Syntax and Semantics

• Part-of-Speech tagging
• Named Entity Recognition
• Syntax parsing techniques
• Dependency parsing
• Semantic analysis
• Grammatical structure understanding
• Advanced linguistic feature extraction

Week 5: Sentiment Analysis

• Sentiment lexicon development
• Machine learning approaches
• Rule-based sentiment techniques
• Deep learning sentiment models
• Emotion detection
• Contextual sentiment analysis
• Practical sentiment modeling

Week 6: Text Classification

• Supervised learning techniques
• Feature extraction methods
• Classification algorithms
• Naive Bayes implementation
• Support Vector Machines
• Neural network text classification
• Multi-class text categorization

Week 7: Topic Modeling

• Latent Dirichlet Allocation
• Non-negative Matrix Factorization
• Clustering text documents
• Semantic topic extraction
• Advanced topic modeling
• Dimensionality reduction
• Practical topic analysis

Week 8: Sequence Models

• Recurrent Neural Networks
• LSTM and GRU architectures
• Sequence-to-sequence modeling
• Machine translation techniques
• Text summarization
• Question-answering systems
• Advanced sequence processing

Week 9: Transformer Models

• Attention mechanisms
• BERT architecture
• GPT model understanding
• Transfer learning
• Large language models
• Contextual embeddings
• Advanced transformer techniques

Week 10: Advanced NLP Techniques

• Named Entity Recognition
• Dependency parsing
• Coreference resolution
• Advanced deep learning models
• Multilingual NLP
• Cross-lingual techniques
• Complex linguistic modeling

Week 11: NLP Applications

• Chatbot development
• Speech recognition
• Information extraction
• Dialogue systems
• Real-world NLP implementations
• Industry use cases
• Practical project development

Week 12: Capstone Project

• End-to-end NLP project
• Industry best practices
• Ethical AI considerations
• Professional networking
• Career guidance
• Final project presentations
• Portfolio development

Learning Outcomes

• Comprehensive NLP technology expertise
• Advanced text processing skills
• Machine learning integration
• Practical implementation knowledge
• Ethical AI development understanding
• Industry-ready NLP capabilities
• Strategic problem-solving techniques

Certification

Students receive a comprehensive Natural Language Processing certification upon successful course completion.

Note: Course content may be dynamically updated to reflect emerging technological trends.