Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Detailed training curriculum
- Introduction to Natural Language Processing (NLP)
- Foundations of NLP
- NLP software frameworks
- Commercial applications of NLP for government
- Web data scraping techniques
- Data retrieval via APIs
- Management and storage of text corpora, including content and metadata preservation
- Benefits of Python and NLTK: A foundational overview
- Practical Application of Corpora and Datasets
- Rationale for using corpora
- Corpus analysis methods
- Data attribute types
- Supported file formats for corpora
- Dataset preparation for NLP applications
- Sentence Structure Analysis
- NLP architectural components
- Natural language understanding processes
- Morphological analysis: stem extraction, tokenization, and part-of-speech tagging
- Syntactic analysis
- Semantic analysis
- Ambiguity resolution strategies
- Text Data Preprocessing
- Raw text corpus operations
- Sentence tokenization
- Text stemming
- Text lemmatization
- Stop word elimination
- Raw sentence corpus operations
- Word tokenization
- Word lemmatization
- Construction of Term-Document and Document-Term matrices
- N-gram and sentence tokenization
- Customized and practical preprocessing workflows
- Raw text corpus operations
- Text Data Analytics
- Core NLP features
- Parsing tools and techniques
- Part-of-speech (POS) tagging systems
- Named Entity Recognition (NER)
- N-gram analysis
- Bag of Words models
- Statistical NLP features
- Linear algebra principles for NLP
- Probabilistic theory applications
- TF-IDF weighting
- Data vectorization
- Encoding and decoding mechanisms
- Data normalization
- Probabilistic modeling
- Advanced Feature Engineering in NLP
- Fundamentals of Word2Vec
- Word2Vec model architecture
- Operational logic of the Word2Vec model
- Conceptual extensions of Word2Vec
- Implementation of Word2Vec models for government data systems
- Case Study: Automated Text Summarization Using Simplified and Standard Luhn Algorithms
- Core NLP features
- Document clustering and pattern mining (including hierarchical clustering and K-means)
- Document comparison and classification using TF-IDF, Jaccard similarity, and cosine distance metrics
- Document classification via Naïve Bayes and Maximum Entropy methods
- Dimensionality reduction techniques: Principal Component Analysis, Singular Value Decomposition, and Non-negative Matrix Factorization
- Topic modeling and information retrieval using Latent Semantic Analysis
- Sentiment degree assessment: positive versus negative classification
- Item Response Theory applications
- POS tagging for entity identification: extraction of persons, locations, and organizations
- Advanced topic modeling via Latent Dirichlet Allocation
- Analysis of unstructured user feedback
- Sentiment classification and visualization of product review data
- Analysis of search logs to identify usage patterns
- Text classification methodologies
- Topic modeling applications
Requirements
Proficiency in natural language processing principles alongside a comprehensive understanding of how artificial intelligence drives business value for government agencies.
21 Hours
Testimonials (1)
Individual support
