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

Comprehensive training curriculum

1. Introduction to NLP
   • Fundamentals of NLP
   • Popular NLP Frameworks
   • Commercial use cases for NLP
   • Extracting data from the web
   • Retrieving text data via various APIs
   • Managing text corpora, including storage of content and associated metadata
   • Benefits of Python and an NLTK crash course
2. Practical Approach to Corpora and Datasets
   • The necessity of a corpus
   • Analyzing corpora
   • Categorization of data attributes
   • Various file formats for corpora
   • Preparing datasets for NLP applications
3. Deconstructing Sentence Structure
   • Core components of NLP
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Managing ambiguity
4. Preprocessing Text Data
   • Corpus: Raw Text
     • Sentence tokenization
     • Stemming raw text
     • Lemmatizing raw text
     • Filtering stop words
   • Corpus: Raw Sentences
     • Word tokenization
     • Word lemmatization
   • Utilizing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Practical and customized preprocessing strategies
5. Text Data Analysis
   • Essential NLP Features
     • Parsers and parsing techniques
     • POS tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words
   • Statistical NLP Features
     • Linear algebra concepts for NLP
     • Probabilistic theory in NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced Feature Engineering in NLP
     • Word2vec fundamentals
     • Components of the Word2vec model
     • Underlying logic of the Word2vec model
     • Expanding the Word2vec concept
     • Applying the Word2vec model
   • Case Study: Bag of Words application for automatic text summarization using simplified and true Luhn's algorithms
6. Document Clustering, Classification, and Topic Modeling
   • Document clustering and pattern mining (e.g., hierarchical clustering, k-means)
   • Document comparison and classification using TFIDF, Jaccard, and cosine distance metrics
   • Classifying documents via Naïve Bayes and Maximum Entropy
7. Identifying Key Text Elements
   • Dimensionality reduction: PCA, SVD, and non-negative matrix factorization
   • Topic modeling and information retrieval via Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
   • Measuring sentiment: Positive vs. Negative
   • Item Response Theory
   • Applying Part of Speech tagging to identify people, places, and organizations
   • Advanced topic modeling with 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