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

Comprehensive training structure

1. Introduction to NLP
   • Grasping NLP concepts
   • NLP frameworks
   • Commercial uses of NLP
   • Extracting data from the web
   • Utilizing diverse APIs to fetch text data
   • Managing text corpora, including content storage and associated metadata
   • Benefits of Python and an NLTK intensive session
2. Practical Insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus examination
   • Categories of data attributes
   • Various file formats for corpora
   • Preparing datasets for NLP tasks
3. Deciphering Sentence Structure
   • Essential NLP components
   • Natural language comprehension
   • Morphological analysis - stemming, 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
     • Eliminating stop words
   • Corpus - raw sentences
     • Word tokenization
     • Word lemmatization
   • Utilizing Term-Document/Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized and practical preprocessing
5. Text Data Analysis
   • Fundamental NLP features
     • Parsers and parsing
     • POS tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words
   • Statistical NLP features
     • Linear algebra concepts for NLP
     • Probabilistic theory for NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering in NLP
     • Word2vec fundamentals
     • Word2vec model components
     • Logic behind the word2vec model
     • Extending the word2vec concept
     • Applications of 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 discovery (hierarchical clustering, k-means, etc.)
   • Document comparison and classification using TFIDF, Jaccard, and cosine distance metrics
   • Document classification using Naïve Bayes and Maximum Entropy
7. Identifying Key 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
   • Positive vs. negative: Sentiment intensity
   • Item Response Theory
   • Part of speech tagging applications: Identifying people, places, and organizations in text
   • Advanced topic modeling: Latent Dirichlet Allocation
9. Case Studies
   • Analyzing unstructured user reviews
   • Sentiment classification and visualization of Product Review Data
   • Mining search logs for usage patterns
   • Text classification
   • Topic modelling