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

Detailed training outline

1. Introduction to NLP
   • Understanding NLP
   • NLP Frameworks
   • Commercial applications of NLP
   • Scraping data from the web
   • Utilizing various APIs to retrieve text data
   • Managing text corpora: saving content and relevant metadata
   • Benefits of using Python and an NLTK crash course
2. Practical Understanding of Corpora and Datasets
   • The need for a corpus
   • Corpus Analysis
   • Types of data attributes
   • Different file formats for corpora
   • Preparing datasets for NLP applications
3. Understanding Sentence Structure
   • Components of NLP
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, speech tags
   • Syntactic analysis
   • Semantic analysis
   • Handling ambiguity
4. Text Data Preprocessing
   • Corpus - Raw text
     • Sentence tokenization
     • Stemming for raw text
     • Lemmization of raw text
     • Stop word removal
   • Corpus - Raw sentences
     • Word tokenization
     • Word lemmatization
   • Working with Term-Document/Document-Term matrices
   • Text tokenization into n-grams and sentences
   • Practical and customized preprocessing
5. Analyzing Text Data
   • Basic features of NLP
     • Parsers and parsing
     • POS tagging and taggers
     • Name entity recognition
     • N-grams
     • Bag of words
   • Statistical features of NLP
     • Concepts of Linear algebra for NLP
     • Probabilistic theory for NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering and NLP
     • Basics of word2vec
     • Components of the word2vec model
     • Logic of the word2vec model
     • Extension of the word2vec concept
     • Application of the word2vec model
   • Case study: Application of bag of words: 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, clustering, 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
   • Reducing dimensionality: Principal Component Analysis, Singular Value Decomposition, and non-negative matrix factorization
   • Topic modeling and information retrieval using Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
   • Positive vs. negative: degree of sentiment
   • Item Response Theory
   • Part-of-speech tagging and its application: identifying people, places, and organizations mentioned in text
   • Advanced topic modeling: Latent Dirichlet Allocation
9. Case Studies
   • Mining unstructured user reviews
   • Sentiment classification and visualization of Product Review Data
   • Mining search logs for usage patterns
   • Text classification
   • Topic modelling