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

Comprehensive training structure

1. Introduction to NLP
   • Core concepts of NLP
   • NLP frameworks and tools
   • Commercial use cases for NLP
   • Data scraping techniques
   • Retrieving text data via various APIs
   • Managing text corpora, including content and metadata storage
   • Benefits of Python and a brief overview of NLTK
2. Practical Insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus analysis techniques
   • Understanding data attributes
   • File formats for corpora
   • Dataset preparation for NLP tasks
3. Deconstructing Sentence Structure
   • Key components of NLP
   • Principles of natural language understanding
   • Morphological analysis: stemming, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Managing linguistic ambiguity
4. Text Data Preprocessing
   • Raw text corpora
     • Sentence tokenization
     • Stemming raw text
     • Lemmatization of raw text
     • Removal of stop words
   • Raw sentence corpora
     • Word tokenization
     • Word lemmatization
   • Utilizing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Implementing custom preprocessing strategies
5. Text Data Analysis
   • Foundational NLP features
     • Parsers and parsing processes
     • Part-of-speech tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag-of-words model
   • Statistical aspects of NLP
     • Linear algebra concepts applied to NLP
     • Probabilistic theory in NLP
     • TF-IDF weighting
     • Vectorization techniques
     • Encoders and decoders
     • Normalization methods
     • Probabilistic models
   • Advanced feature engineering and NLP
     • Introduction to word2vec
     • Architectural components of the word2vec model
     • Theoretical logic behind word2vec
     • Extending the word2vec concept
     • Practical applications of the word2vec model
   • Case study: Applying bag-of-words for automatic text summarization using simplified and authentic Luhn's algorithms
6. Document Clustering, Classification, and Topic Modeling
   • Document clustering and pattern discovery (including hierarchical clustering, k-means, etc.)
   • Document comparison and classification using TF-IDF, Jaccard, and cosine distance metrics
   • Classifying documents with Naïve Bayes and Maximum Entropy
7. Extracting Key Text Elements
   • Dimensionality reduction via 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
   • Distinguishing positive vs. negative sentiment intensity
   • Item Response Theory
   • Applying part-of-speech tagging to identify people, places, and organizations
   • Advanced topic modeling with Latent Dirichlet Allocation
9. Applied Case Studies
   • Analyzing unstructured user reviews
   • Sentiment classification and visualization of product review data
   • Extracting usage patterns from search logs
   • Text classification projects
   • Topic modeling exercises