Natural Language Processing (NLP) - AI/Robotics Training Course

Detailed Training Outline 1. Natural Language Processing Fundamentals - Overview of Natural Language Processing (NLP) - NLP Architectural Frameworks - Commercial applications of NLP for government - Web-based data acquisition methods - Utilizing Application Programming Interfaces (APIs) for text data retrieval - Management and storage of text corpora, including metadata preservation - Operational advantages of Python and an introductory NLTK workshop 2. Corpus and Dataset Management - Rationale for corpus utilization - Corpus analytical methodologies - Data attribute classification - Standard file formats for textual corpora - Dataset preparation protocols for NLP applications 3. Linguistic Structure and Sentence Analysis - NLP operational components - Principles of natural language comprehension - Morphological analysis: stemming, tokenization, and part-of-speech tagging - Syntactic analysis - Semantic analysis - Strategies for resolving linguistic ambiguity 4. Text Data Preprocessing - Raw text corpus preparation - Sentence tokenization - Text stemming - Text lemmatization - Exclusion of stop words - Raw sentence corpus processing - Word tokenization - Word lemmatization - Construction of Term-Document and Document-Term matrices - Generation of n-grams and sentence tokens - Implementation of custom preprocessing workflows 5. Text Data Analytics - Fundamental NLP features - Parsing mechanisms - Part-of-speech tagging - Named Entity Recognition (NER) - N-gram analysis - Bag-of-words models - Statistical NLP concepts - Linear algebra applications in NLP - Probabilistic theory in NLP - Term Frequency-Inverse Document Frequency (TF-IDF) - Vectorization techniques - Encoder and decoder architectures - Data normalization - Probabilistic modeling - Advanced feature engineering and NLP - Fundamentals of Word2Vec - Word2Vec model architecture - Operational logic of Word2Vec - Conceptual extensions of Word2Vec - Practical applications of Word2Vec - Case Study: Automated text summarization utilizing simplified and standard Luhn algorithms 6. Document Clustering, Classification, and Topic Modeling - Document clustering and pattern mining (hierarchical clustering, k-means, etc.) - Document classification and comparison using TF-IDF, Jaccard, and cosine distance metrics - Document classification utilizing Naïve Bayes and Maximum Entropy models 7. Identification of Key Text Elements - Dimensionality reduction techniques: Principal Component Analysis, Singular Value Decomposition, and Non-negative Matrix Factorization - Information retrieval and topic modeling using Latent Semantic Analysis 8. Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling - Sentiment intensity assessment (positive vs. negative) - Item Response Theory - Part-of-speech tagging applications for identifying persons, locations, and organizations - Advanced topic modeling: Latent Dirichlet Allocation 9. Case Studies - Analysis of unstructured user reviews - Sentiment classification and visualization of product review data - Extraction of usage patterns from search logs - Text classification methodologies - Topic modeling applications