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

Comprehensive training curriculum

1. Introduction to NLP
   • Foundations of NLP
   • Overview of NLP Frameworks
   • Commercial use cases for NLP
   • Web data scraping techniques
   • Interacting with various APIs to extract text data
   • Managing and storing text corpora with relevant metadata
   • Benefits of using Python and an NLTK overview
2. Practical understanding of Corpora and Datasets
   • The necessity of a corpus
   • Conducting Corpus Analysis
   • Categorizing data attributes
   • Various file formats for corpora
   • Dataset preparation for NLP workflows
3. Understanding Sentence Structure
   • Core components of NLP
   • Natural language comprehension
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Managing ambiguity
4. Text Data Preprocessing
   • Raw Text Corpus Handling
     • Sentence tokenization
     • Stemming raw text
     • Lemmatization of raw text
     • Filtering stop words
   • Raw Sentence Corpus Handling
     • Word tokenization
     • Word lemmatization
   • Constructing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Implementing practical and custom preprocessing steps
5. Analyzing Text Data
   • Foundational NLP Features
     • Parsers and parsing techniques
     • Part-of-speech (POS) tagging and taggers
     • Named Entity Recognition (NER)
     • N-grams
     • Bag of Words model
   • Statistical NLP Features
     • Linear algebra concepts for NLP
     • Probabilistic theory applications in NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced Feature Engineering in NLP
     • Introduction to word2vec
     • Components of the word2vec model
     • Underlying logic of word2vec
     • Extending the word2vec concept
     • Applying the word2vec model
   • Case Study: Bag of Words application for automatic text summarization using simplified and authentic Luhn's algorithms
6. Document Clustering, Classification, and Topic Modeling
   • Document clustering and pattern mining (e.g., hierarchical clustering, k-means)
   • Comparing and classifying documents via TFIDF, Jaccard, and cosine similarity
   • Document classification using 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
   • Distinguishing positive vs. negative sentiment intensity
   • Item Response Theory
   • POS tagging applications: identifying people, places, and organizations
   • Advanced topic modeling with Latent Dirichlet Allocation
9. Case Studies
   • Extracting insights from unstructured user reviews
   • Sentiment classification and visualization of product review data
   • Analyzing search logs for usage patterns
   • Text classification projects
   • Topic modeling projects