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

Detailed training outline

1. Introduction to NLP
   • Fundamentals of NLP
   • NLP Frameworks
   • Commercial use cases for NLP
   • Web data scraping techniques
   • Utilizing various APIs to acquire text data
   • Managing text corpora, including content storage and metadata handling
   • Benefits of using Python and an NLTK crash course
2. Practical Understanding of a Corpus and Dataset
   • The necessity of a corpus
   • Corpus Analysis techniques
   • Categorization of data attributes
   • Various file formats for corpora
   • Dataset preparation for NLP applications
3. Understanding the Structure of a Sentences
   • Core components of NLP
   • Natural language understanding
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Addressing ambiguity
4. Text data preprocessing
   • Raw text corpus handling
     • Sentence tokenization
     • Stemming raw text
     • Lemmatization of raw text
     • Stop word elimination
   • Raw sentence corpus handling
     • 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 mechanisms
     • Part-of-speech (POS) tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words approach
   • Statistical features in NLP
     • Linear algebra concepts applied to NLP
     • Probabilistic theory in NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering and NLP
     • Foundations of word2vec
     • Internal components of the word2vec model
     • Operational logic of the word2vec model
     • Extensions of the word2vec concept
     • Practical application of the word2vec model
   • Case study: Applying bag of words for automatic text summarization using simplified and original Luhn's algorithms
6. Document Clustering, Classification and Topic Modeling
   • Document clustering and pattern mining (including hierarchical clustering and k-means)
   • Document comparison and classification using TF-IDF, Jaccard, and cosine distance metrics
   • Document classification via 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 utilizing Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis and Advanced Topic Modeling
   • Differentiating positive vs. negative sentiment degrees
   • Item Response Theory
   • Part-of-speech tagging applications: extracting people, places, and organizations from text
   • Advanced topic modeling: Latent Dirichlet Allocation
9. Case studies
   • Extracting insights from unstructured user reviews
   • Sentiment classification and visualization of Product Review Data
   • Mining search logs to identify usage patterns
   • Text classification
   • Topic modelling