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

Comprehensive course syllabus

1. Introduction to NLP
   • Core concepts of NLP
   • Overview of NLP frameworks
   • Commercial uses of NLP
   • Web data scraping techniques
   • Using various APIs to acquire textual data
   • Managing text corpora and saving associated content and metadata
   • Benefits of Python and a quick guide to NLTK
2. Practical Insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus analysis techniques
   • Categorizing data attributes
   • Varying file formats for corpora
   • Preparing datasets for NLP tasks
3. Deconstructing Sentence Structure
   • Key components of NLP
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Resolving ambiguity
4. Text Data Preprocessing
   • Corpus - Raw text
     • Sentence tokenization
     • Stemming raw text
     • Lemmatizing raw text
     • Eliminating stop words
   • Corpus - Raw sentences
     • Word tokenization
     • Word lemmatization
   • Managing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Implementing custom preprocessing strategies
5. Text Data Analysis
   • Fundamental NLP features
     • Parsers and parsing techniques
     • POS tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words
   • Statistical aspects of NLP
     • Linear algebra concepts for NLP
     • Probabilistic theories in NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering and NLP
     • Foundations of word2vec
     • Components of the word2vec model
     • Logic behind the word2vec model
     • Extending the word2vec concept
     • Applications of the word2vec model
   • Case study: Bag of words application: Automatic text summarization using simplified and true Luhn's algorithms
6. Document Clustering, Classification, and Topic Modeling
   • Document clustering and pattern mining (including hierarchical and k-means clustering)
   • Comparing and classifying documents 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
   • Assessing sentiment intensity: Positive vs. negative
   • Item Response Theory
   • Applying part-of-speech tagging to locate entities like 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
   • Mining search logs to identify usage patterns
   • Text classification projects
   • Topic modeling exercises