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

Comprehensive training curriculum

1. Foundations of NLP
   • Core concepts of NLP
   • Overview of NLP frameworks
   • Commercial uses of NLP
   • Data scraping techniques for the web
   • Utilizing various APIs to acquire text data
   • Managing text corpora: storing content and associated metadata
   • Benefits of Python and an NLTK overview
2. Practical Insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus analysis methods
   • Categorizing data attributes
   • File formats suitable for corpora
   • Dataset preparation for NLP tasks
3. Analyzing Sentence Structure
   • Key components of NLP
   • Natural language comprehension
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Addressing ambiguity
4. Text Data Preprocessing
   • Corpus - Raw Text
     • Sentence tokenization
     • Stemming raw text
     • Lemmatizing raw text
     • Removing stop words
   • Corpus - Raw Sentences
     • Word tokenization
     • Word lemmatization
   • Managing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Practical and tailored preprocessing strategies
5. Text Data Analysis
   • Essential NLP features
     • Parsers and parsing techniques
     • Part-of-speech tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag-of-words model
   • Statistical aspects of NLP
     • Linear algebra concepts in NLP
     • Probabilistic theory for NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced Feature Engineering and NLP
     • Introduction to word2vec
     • Components of the word2vec model
     • Underlying logic of the word2vec model
     • Extensions of the word2vec concept
     • Applications of the word2vec model
   • Case study: Automatic text summarization using the Bag of Words method with simplified and standard Luhn's algorithms
6. Document Clustering, Classification, and Topic Modeling
   • Document clustering and pattern mining (hierarchical, k-means, etc.)
   • Document comparison and classification using TFIDF, Jaccard, and cosine similarity
   • Document classification using Naïve Bayes and Maximum Entropy
7. Identifying Key Text Elements
   • Dimensionality reduction: PCA, 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 and intensity
   • Item Response Theory
   • Applying Part-of-Speech tagging to identify 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
   • Extracting usage patterns from search logs
   • Text classification tasks
   • Topic modeling exercises