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

Comprehensive training syllabus

1. Introduction to NLP
   • Core concepts of NLP
   • Overview of NLP frameworks
   • Commercial use cases for NLP
   • Web data scraping techniques
   • Retrieving text data via various APIs
   • Managing text corpora: storing content and associated metadata
   • Benefits of Python and an introductory NLTK session
2. Practical Understanding of a Corpus and Dataset
   • The necessity of a corpus
   • Analyzing corpora
   • Varieties of data attributes
   • File formats for corpora
   • Preparing datasets for NLP workflows
3. Understanding the Structure of Sentences
   • Fundamental NLP components
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Managing ambiguity
4. Text Data Preprocessing
   • Raw text corpus
     • Sentence tokenization
     • Stemming raw text
     • Lemmatizing raw text
     • Removing stop words
   • Raw sentence corpus
     • Word tokenization
     • Word lemmatization
   • Managing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized and practical preprocessing strategies
5. Analyzing Text Data
   • Foundational 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 and NLP
     • word2vec fundamentals
     • Components of the word2vec model
     • The underlying logic of word2vec
     • Extending the word2vec concept
     • Applying the word2vec model
   • Case study: Applying bag of words to 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
   • Document classification via Naïve Bayes and Maximum Entropy
7. Identifying Important Text Elements
   • Dimensionality reduction: PCA, SVD, 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 degrees
   • Item Response Theory
   • Applying POS tagging to identify people, places, and organizations
   • Advanced topic modeling with Latent Dirichlet Allocation
9. Case Studies
   • Mining unstructured user reviews
   • Sentiment classification and visualization of product review data
   • Analyzing search logs for usage patterns
   • Text classification
   • Topic modelling