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

Comprehensive training curriculum

1. Foundations of NLP
   • Concepts behind NLP
   • NLP Frameworks
   • Commercial use cases of NLP
   • Web data scraping techniques
   • Utilizing APIs to retrieve text data
   • Managing text corpora: storing content and associated metadata
   • Benefits of using Python and an introductory NLTK session
2. Practical Insights into Corpora and Datasets
   • The necessity of a corpus
   • Corpus Analysis
   • Categories of data attributes
   • File formats for corpora
   • Preparing datasets for NLP applications
3. Analyzing Sentence Structure
   • 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
   • Raw text corpus
     • Sentence tokenization
     • Stemming raw text
     • Lemmatization of raw text
     • Removal of stop words
   • Raw sentence corpus
     • Word tokenization
     • Word lemmatization
   • Handling Term-Document/Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized and practical preprocessing strategies
5. Text Data Analysis
   • Fundamental NLP features
     • Parsers and parsing techniques
     • Part-of-speech (POS) tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words
   • Statistical aspects of NLP
     • Linear algebra concepts applied to NLP
     • Probabilistic theory in NLP
     • TF-IDF
     • Vectorization
     • Encoders and Decoders
     • Normalization
     • Probabilistic Models
   • Advanced feature engineering in NLP
     • Introduction to word2vec
     • Model components of word2vec
     • Underlying logic of the word2vec model
     • Extensions of the word2vec concept
     • Applying the word2vec model
   • Case study: Applying bag of words for automatic text summarization using simplified and accurate 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 similarity 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
   • Polarity: degrees of sentiment (positive vs. negative)
   • 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
   • Extracting usage patterns from search logs
   • Text classification
   • Topic modeling