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

Comprehensive training outline

1. Introduction to NLP
   • Concepts of NLP
   • NLP Frameworks
   • Commercial use cases for NLP
   • Web data scraping
   • Utilizing various APIs to acquire text data
   • Managing text corpora: storing content and associated metadata
   • Benefits of Python and a brief NLTK introduction
2. Practical Insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus Analysis
   • Categorization of data attributes
   • Various file formats for corpora
   • Dataset preparation for NLP tasks
3. Sentence Structure Analysis
   • Core components of NLP
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Addressing ambiguity
4. Text Data Preprocessing
   • Raw text corpus
     • Sentence tokenization
     • Stemming of raw text
     • Lemmatization of raw text
     • Removal of stop words
   • Raw sentence corpus
     • Word tokenization
     • Word lemmatization
   • Constructing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized and practical preprocessing strategies
5. Text Data Analysis
   • Foundational NLP features
     • Parsers and parsing techniques
     • POS tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words
   • Statistical aspects of NLP
     • Linear algebra concepts for NLP
     • Probabilistic theory in NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering in NLP
     • Foundations of word2vec
     • Architectural components of word2vec
     • Operational logic of word2vec
     • Extensions of the word2vec concept
     • Implementations of the word2vec model
   • Case Study: Automatic text summarization using simplified and true Luhn's algorithms via bag of words
6. Document Clustering, Classification, and Topic Modelling
   • Document clustering and pattern detection (hierarchical clustering, k-means, etc.)
   • Document comparison and classification using TFIDF, Jaccard, and cosine distance metrics
   • Document classification with Naïve Bayes and Maximum Entropy
7. Identifying Key Text Elements
   • Dimensionality reduction: PCA, Singular Value Decomposition, and non-negative matrix factorization
   • Topic modelling and information retrieval via Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis, and Advanced Topic Modelling
   • Determining positive vs. negative sentiment degrees
   • Item Response Theory
   • POS tagging applications: identifying people, places, and organizations in text
   • Advanced topic modelling: Latent Dirichlet Allocation
9. Case Studies
   • Analyzing unstructured user reviews
   • Sentiment classification and visualization of product review data
   • Extracting usage patterns from search logs
   • Text classification
   • Topic modelling