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

Comprehensive training curriculum

1. Introduction to NLP
   • Concepts of NLP
   • NLP Frameworks
   • Commercial use cases for NLP
   • Web scraping techniques
   • Utilizing APIs to fetch text data
   • Managing text corpora: saving content and associated metadata
   • Benefits of Python and a brief NLTK overview
2. Practical Insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus Analysis
   • Categorization of data attributes
   • Supported file formats for corpora
   • Preparing datasets for NLP tasks
3. Analyzing Sentence Structure
   • Core components of NLP
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, and part-of-speech tags
   • Syntactic analysis
   • Semantic analysis
   • Addressing linguistic ambiguity
4. Preprocessing Text Data
   • Corpus - Raw Text
     • Sentence tokenization
     • Stemming raw text
     • Lemmatization of raw text
     • Stop word removal
   • Corpus - Raw Sentences
     • Word tokenization
     • Word lemmatization
   • Handling Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized and practical preprocessing workflows
5. Analyzing Text Data
   • Fundamental 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 theories in NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering in NLP
     • Foundations of word2vec
     • Architecture of the word2vec model
     • Underlying logic of the word2vec model
     • Extensions of the word2vec concept
     • Applications of the word2vec model
   • Case study: Applying bag of words for automatic text summarization using simplified and full Luhn's algorithms
6. Document Clustering, Classification, and Topic Modeling
   • Document clustering and pattern mining (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: Principal Component Analysis, Singular Value Decomposition, and non-negative matrix factorization
   • Topic modeling and information retrieval via Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
   • Positive vs. negative sentiment intensity
   • Item Response Theory
   • Application of part-of-speech tagging: identifying people, places, and organizations
   • Advanced topic modeling: Latent Dirichlet Allocation
9. Case Studies
   • Analyzing unstructured user reviews
   • Sentiment classification and visualization of Product Review Data
   • Mining search logs to identify usage patterns
   • Text classification
   • Topic modelling