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

Comprehensive training curriculum

1. Introduction to NLP
   • Foundations of NLP
   • Overview of NLP frameworks
   • Commercial use cases for NLP
   • Web data scraping techniques
   • Utilizing various APIs to fetch text data
   • Managing text corpora: storing content and associated metadata
   • Benefits of using Python and an NLTK introduction
2. Practical insights into Corpora and Datasets
   • The necessity of a corpus in NLP
   • Techniques for corpus analysis
   • Categorization of data attributes
   • Common file formats for corpora
   • Dataset preparation strategies for NLP applications
3. Analyzing Sentence Structure
   • Core components of NLP
   • Natural language understanding principles
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis methods
   • Semantic analysis approaches
   • Strategies for handling ambiguity
4. Text Data Preprocessing
   • Raw Text Corpus
     • Sentence tokenization
     • Applying stemming to raw text
     • Lemmatization of raw text
     • Removal of stop words
   • Raw Sentence Corpus
     • Word tokenization
     • Word lemmatization
   • Managing Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentence segments
   • Customized and practical preprocessing workflows
5. Analyzing Textual Data
   • Essential NLP features
     • Parsers and parsing techniques
     • Part-of-speech tagging and taggers
     • Named entity recognition
     • Utilizing n-grams
     • The Bag of Words model
   • Statistical aspects of NLP
     • Linear algebra concepts relevant to NLP
     • Probabilistic theories applied in NLP
     • TF-IDF weighting
     • Text vectorization
     • Encoders and decoders
     • Data normalization
     • Probabilistic modeling
   • Advanced feature engineering and NLP
     • Fundamentals of word2vec
     • Architecture of the word2vec model
     • Logical mechanics of word2vec
     • Extensions of word2vec concepts
     • Real-world applications of the word2vec model
   • Case study: Bag of Words application for 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 TF-IDF, Jaccard, and cosine similarity metrics
   • Classification techniques using Naïve Bayes and Maximum Entropy
7. Identifying Key Textual Elements
   • Dimensionality reduction techniques: PCA, SVD, and Non-negative Matrix Factorization
   • Topic modeling and information retrieval via Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
   • Distinguishing positive from negative sentiment intensity
   • Item Response Theory
   • Applying Part-of-Speech tagging to identify people, places, and organizations
   • Advanced topic modeling using 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 workflows
   • Topic modeling exercises