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

Comprehensive training curriculum

1. Introduction to NLP
   • Concepts of NLP
   • NLP Frameworks
   • Commercial uses of NLP
   • Web data scraping
   • Retrieving text data via various APIs
   • Managing text corpora: storing content and associated metadata
   • Benefits of Python and an NLTK introductory session
2. Practical Understanding of a Corpus and Dataset
   • The necessity of a corpus
   • Corpus Analysis
   • Categories of data attributes
   • File formats for corpora
   • Preparing datasets for NLP applications
3. Understanding the Structure of Sentences
   • Core components of NLP
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Managing 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
   • Utilizing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Tailored and practical preprocessing techniques
5. Analyzing Text Data
   • Basic features of NLP
     • Parsers and parsing processes
     • POS tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words
   • Statistical features in NLP
     • 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
     • Logic behind the word2vec model
     • Expanding the word2vec concept
     • Applying the word2vec model
   • Case study: Applying bag of words for automatic text summarization using simplified and true Luhn's algorithms
6. Document Clustering, Classification, and Topic Modeling
   • Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
   • Comparing and classifying documents using TFIDF, Jaccard, and cosine distance measures
   • Document classification using Naïve Bayes and Maximum Entropy
7. Identifying Important Text Elements
   • Dimensionality reduction: Principal Component Analysis, 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
   • Distinguishing positive from negative sentiment degrees
   • Item Response Theory
   • Part-of-speech tagging applications: identifying people, places, and organizations in text
   • Advanced topic modeling: Latent Dirichlet Allocation
9. Case Studies
   • Extracting insights from unstructured user reviews
   • Classifying and visualizing sentiment in product review data
   • Analyzing search logs for usage patterns
   • Text classification
   • Topic modeling