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

Comprehensive training outline

1. Introduction to NLP
   • Concepts of NLP
   • Popular NLP frameworks
   • Commercial uses of NLP
   • Web data scraping techniques
   • Utilising APIs to fetch text data
   • Managing and storing text corpora with relevant metadata
   • Benefits of Python and an NLTK quick start
2. Practical Understanding of a Corpus and Dataset
   • The necessity of a corpus
   • Corpus analysis methods
   • Varieties of data attributes
   • File formats suitable for corpora
   • Dataset preparation for NLP tasks
3. Understanding the Structure of Sentences
   • Core NLP components
   • Natural language understanding
   • Morphological analysis: stemming, 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
     • Removal of stop words
   • Raw sentence corpus
     • Word tokenization
     • Word lemmatization
   • Handling Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized and practical preprocessing strategies
5. Analyzing Text data
   • Fundamental NLP features
     • Parsers and parsing techniques
     • POS tagging and taggers
     • Name entity recognition
     • N-grams
     • Bag of words approach
   • Statistical aspects of NLP
     • Linear algebra concepts for NLP
     • Probabilistic theory in NLP
     • TF-IDF
     • Vectorization techniques
     • Encoders and Decoders
     • Data normalization
     • Probabilistic Models
   • Advanced feature engineering in NLP
     • Foundations of word2vec
     • Structural components of the word2vec model
     • Operational logic of word2vec
     • Extensions of the word2vec concept
     • Practical applications of the word2vec model
   • Case study: Bag of words application in automatic text summarization using simplified and true Luhn's algorithms
6. Document Clustering, Classification and Topic Modeling
   • Document clustering and pattern discovery (including hierarchical and k-means clustering)
   • Document comparison and classification using TFIDF, Jaccard, and cosine similarity
   • Document classification via Naïve Bayes and Maximum Entropy
7. Identifying Important Text Elements
   • Dimensionality reduction: PCA, SVD, and non-negative matrix factorization
   • Topic modelling and information retrieval through Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis and Advanced Topic Modeling
   • Distinguishing positive and negative sentiment
   • Item Response Theory
   • POS tagging applications: identifying people, places, and organizations
   • Advanced topic modelling: Latent Dirichlet Allocation
9. Case studies
   • Extracting insights from unstructured user reviews
   • Sentiment classification and visualization of product review data
   • Analysing search logs to identify usage patterns
   • Text classification techniques
   • Topic modelling practices