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

Comprehensive training curriculum

1. Introduction to NLP
   • Concepts in NLP
   • NLP Frameworks
   • Commercial use cases for NLP
   • Web data scraping
   • Utilizing various APIs to fetch text data
   • Managing and storing text corpora, including content and associated metadata
   • Benefits of Python and an NLTK introductory session
2. Practical insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus Analysis
   • Categorization of data attributes
   • Various file formats for corpora
   • Preparing datasets for NLP applications
3. Comprehending Sentence Structure
   • Core components of NLP
   • Natural language understanding
   • Morphological analysis - stems, words, tokens, speech tags
   • Syntactic analysis
   • Semantic analysis
   • Managing ambiguity
4. Text data preprocessing
   • Corpus: Raw text
     • Sentence tokenization
     • Stemming for raw text
     • Lemmization of raw text
     • Stop word removal
   • Corpus: Raw sentences
     • Word tokenization
     • Word lemmatization
   • Working with Term-Document and Document-Term matrices
   • Text tokenization into n-grams and sentences
   • Practical and custom preprocessing strategies
5. Analyzing Text data
   • Fundamental features of NLP
     • 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 for NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering and NLP
     • Foundations of word2vec
     • Components of the word2vec model
     • Logic behind the word2vec model
     • Extensions of the word2vec concept
     • Applications of the word2vec model
   • Case study: Application of bag of words: automatic text summarization using simplified and true Luhn's algorithms
6. Document Clustering, Classification and Topic Modeling
   • Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
   • Comparing and classifying documents using TFIDF, Jaccard, and cosine distance measures
   • Document classification using Naïve Bayes and Maximum Entropy
7. Identifying Important Text Elements
   • Dimensionality reduction: Principal Component Analysis, Singular Value Decomposition, and non-negative matrix factorization
   • Topic modeling and information retrieval using Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis and Advanced Topic Modeling
   • Positive vs. negative: sentiment intensity
   • Item Response Theory
   • Part of speech tagging and its application: identifying people, places, and organizations in text
   • Advanced topic modeling: Latent Dirichlet Allocation
9. Case studies
   • Mining unstructured user reviews
   • Sentiment classification and visualization of Product Review Data
   • Mining search logs for usage patterns
   • Text classification
   • Topic modelling