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Course Outline
Detailed Training Outline
- Introduction to NLP
- Understanding NLP for government applications
- NLP Frameworks and their relevance for government
- Commercial applications of NLP in the public sector
- Scraping data from web sources for government use
- Working with various APIs to retrieve text data for government projects
- Working and storing text corpora, saving content and relevant metadata for government records
- Advantages of using Python and NLTK for government NLP tasks
- Practical Understanding of a Corpus and Dataset
- Purpose of a corpus in government data analysis
- Corpus Analysis techniques for government applications
- Types of data attributes relevant to government datasets
- Different file formats for corpora used in government projects
- Preparing a dataset for NLP applications in the public sector
- Understanding the Structure of Sentences
- Key components of NLP for government use
- Natural language understanding in government contexts
- Morphological analysis: stem, word, token, and speech tags for government data
- Syntactic analysis methods for government text
- Semantic analysis techniques applicable to government documents
- Handling ambiguity in government text data
- Text Data Preprocessing
- Corpus: Raw Text
- Sentence tokenization for government documents
- Stemming of raw text for government data
- Lemmization of raw text for government use
- Stop word removal in government datasets
- Corpus: Raw Sentences
- Word tokenization for government text
- Word lemmatization for government documents
- Working with Term-Document/Document-Term matrices for government data
- Text tokenization into n-grams and sentences for government applications
- Practical and customized preprocessing methods for government datasets
- Corpus: Raw Text
- Analyzing Text Data
- Basic Features of NLP for Government Use
- Parsers and parsing techniques for government text
- POS tagging and taggers in government data analysis
- Name entity recognition for government applications
- N-grams in government text analysis
- Bag of words method for government documents
- Statistical Features of NLP for Government Use
- Concepts of Linear Algebra for NLP in government projects
- Probabilistic theory for NLP in government applications
- TF-IDF calculations for government text data
- Vectorization techniques for government datasets
- Encoders and decoders in government NLP tasks
- Normalization methods for government text
- Probabilistic models for government data analysis
- Advanced Feature Engineering and NLP for Government Use
- Basics of word2vec for government applications
- Components of the word2vec model in government contexts
- Logic of the word2vec model for government text
- Extensions of the word2vec concept for government use
- Applications of the word2vec model in government data analysis
- Case Study: Application of Bag of Words for Automatic Text Summarization Using Simplified and True Luhn's Algorithms in Government Documents
- Basic Features of NLP for Government Use
- Document Clustering, Classification, and Topic Modeling for Government Use
- Document clustering and pattern mining techniques (hierarchical clustering, k-means, etc.) for government data
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance measures for government applications
- Document classification using Naïve Bayes and Maximum Entropy in government contexts
- Identifying Important Text Elements for Government Data
- Reducing dimensionality: Principal Component Analysis, Singular Value Decomposition, non-negative matrix factorization for government text data
- Topic modeling and information retrieval using Latent Semantic Analysis for government documents
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling for Government Use
- Positive vs. negative: degree of sentiment in government data
- Item Response Theory for government text analysis
- Part of speech tagging and its application: finding people, places, and organizations mentioned in government documents
- Advanced topic modeling: Latent Dirichlet Allocation for government applications
- Case Studies
- Mining unstructured user reviews for government insights
- Sentiment classification and visualization of Product Review Data for government use
- Mining search logs for usage patterns in government systems
- Text classification for government applications
- Topic modeling for government data analysis
Requirements
Understanding of NLP principles and recognition of AI applications for government
21 Hours
Testimonials (1)
I feel I get the core skills I need to understand how the ROS fits together, and how to structure projects in it.
