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Course Outline
Detailed Training Outline
- Introduction to NLP
- Understanding NLP
- NLP Frameworks
- Commercial Applications of NLP for Government
- Scraping Data from the Web for Government Use
- Working with Various APIs to Retrieve Text Data for Government
- Working and Storing Text Corpora: Saving Content and Relevant Metadata
- Advantages of Using Python and NLTK: A Crash Course for Government Professionals
- Practical Understanding of a Corpus and Dataset
- Why Do We Need a Corpus?
- Corpus Analysis for Government Applications
- Types of Data Attributes in Government Datasets
- Different File Formats for Corpora in Government Contexts
- Preparing a Dataset for NLP Applications in Government
- Understanding the Structure of Sentences
- Components of NLP for Government Use
- Natural Language Understanding for Government Operations
- Morphological Analysis: Stem, Word, Token, and Speech Tags for Government Texts
- Syntactic Analysis for Government Documents
- Semantic Analysis for Government Communications
- Handling Ambiguity in Government Text Data
- Text Data Preprocessing
- Corpus - Raw Text
- Sentence Tokenization for Government Documents
- Stemming for Raw Text in Government Contexts
- Lemmization of Raw Text for Government Use
- Stop Word Removal for Government Data
- Corpus - Raw Sentences
- Word Tokenization for Government Texts
- Word Lemmatization for Government Documents
- Working with Term-Document/Document-Term Matrices for Government Analysis
- Text Tokenization into N-grams and Sentences for Government Use
- Practical and Customized Preprocessing for Government Data
- Corpus - Raw Text
- Analyzing Text Data
- Basic Features of NLP for Government Analysis
- Parsers and Parsing for Government Texts
- POS Tagging and Tagger Applications for Government Use
- Name Entity Recognition for Government Documents
- N-grams for Government Data Analysis
- Bag of Words for Government Text Processing
- Statistical Features of NLP for Government Applications
- Concepts of Linear Algebra for NLP in Government Contexts
- Probabilistic Theory for NLP in Government Data Analysis
- TF-IDF for Government Texts
- Vectorization Techniques for Government Documents
- Encoders and Decoders for Government Use
- Normalization of Government Data
- Probabilistic Models for Government Text Analysis
- Advanced Feature Engineering and NLP for Government
- Basics of Word2Vec for Government Applications
- Components of the Word2Vec Model for Government Use
- Logic of the Word2Vec Model in Government Contexts
- Extensions of the Word2Vec Concept for Government Data
- Applications of the Word2Vec Model for Government Analysis
- Case Study: Application of Bag of Words for Automatic Text Summarization Using Simplified and True Luhn's Algorithms in Government Contexts
- Basic Features of NLP for Government Analysis
- Document Clustering, Classification, and Topic Modeling
- Document Clustering and Pattern Mining (Hierarchical Clustering, K-means, etc.) for Government Use
- Comparing and Classifying Documents Using TFIDF, Jaccard, and Cosine Distance Measures in Government Contexts
- Document Classification Using Naïve Bayes and Maximum Entropy for Government Applications
- Identifying Important Text Elements
- Reducing Dimensionality: Principal Component Analysis, Singular Value Decomposition, Non-negative Matrix Factorization for Government Data
- Topic Modeling and Information Retrieval Using Latent Semantic Analysis for Government Use
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Positive vs. Negative: Degree of Sentiment in Government Texts
- Item Response Theory for Government Applications
- Part of Speech Tagging and Its Application: Finding People, Places, and Organizations Mentioned in Government Texts
- Advanced Topic Modeling: Latent Dirichlet Allocation for Government Use
- Case Studies
- Mining Unstructured User Reviews for Government Insights
- Sentiment Classification and Visualization of Product Review Data for Government Analysis
- Mining Search Logs for Usage Patterns in Government Contexts
- Text Classification for Government Documents
- Topic Modeling for Government Data
Requirements
Understanding the principles of Natural Language Processing (NLP) and recognizing the applications of Artificial Intelligence (AI) for government operations is essential. This knowledge enhances the efficiency and effectiveness of public sector workflows, governance, and accountability.
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.
