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

Comprehensive course curriculum

1. Foundations of NLP
   • Core concepts of NLP
   • Overview of NLP frameworks
   • Commercial use cases for NLP
   • Techniques for extracting data from the web
   • Utilizing diverse APIs to acquire textual data
   • Managing text corpora by saving content along with associated metadata
   • Benefits of using Python and an introductory NLTK session
2. Hands-on Exploration of Corpora and Datasets
   • The importance of maintaining a corpus
   • Analyzing corpus data
   • Categorizing data attributes
   • File formats suitable for corpora
   • Preparing datasets specifically for NLP tasks
3. Deconstructing Sentence Structure
   • Key components of NLP
   • Mechanisms of natural language understanding
   • Morphological analysis covering stems, words, tokens, and part-of-speech tags
   • Syntactic processing
   • Semantic interpretation
   • Strategies for resolving ambiguity
4. Preprocessing Textual Data
   • Handling raw text corpora
     • Sentence segmentation
     • Applying stemming to raw text
     • Lemmizing raw text
     • Filtering out stop words
   • Processing raw sentence structures
     • Word-level tokenization
     • Word lemmatization
   • Constructing Term-Document and Document-Term matrices
   • Breaking text down into n-grams and sentences
   • Implementing practical and customized preprocessing strategies
5. Deep Dive into Text Analysis
   • Fundamental NLP capabilities
     • Parsers and parsing techniques
     • POS tagging and the role of taggers
     • Recognizing named entities
     • Utilizing n-grams
     • The Bag of Words approach
   • Statistical aspects of NLP
     • Linear algebra concepts relevant to NLP
     • Probabilistic theories in NLP
     • TF-IDF weighting
     • Data vectorization
     • Working with encoders and decoders
     • Normalization procedures
     • Application of probabilistic models
   • Advanced feature engineering and NLP techniques
     • Introduction to word2vec
     • Architectural components of the word2vec model
     • Operational logic of word2vec
     • Extensions of the word2vec concept
     • Practical applications of the word2vec model
   • Case Study: Leveraging Bag of Words for automated text summarization using simplified and standard Luhn's algorithms
6. Clustering, Classification, and Topic Modeling of Documents
   • Document clustering and pattern discovery (including hierarchical clustering, k-means, and other methods)
   • Comparing and categorizing documents using TFIDF, Jaccard, and cosine distance metrics
   • Document classification via Naïve Bayes and Maximum Entropy models
7. Extracting Key Textual Elements
   • Dimensionality reduction techniques: Principal Component Analysis, Singular Value Decomposition, and non-negative matrix factorization
   • Topic modeling and information retrieval through Latent Semantic Analysis
8. Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
   • Assessing sentiment intensity from positive to negative
   • Item Response Theory
   • Applying POS tagging to identify people, locations, and organizations within text
   • Advanced topic modeling using Latent Dirichlet Allocation
9. Practical Case Studies
   • Analyzing unstructured user reviews
   • Classifying and visualizing sentiment in product review data
   • Extracting usage patterns from search logs
   • Text classification workflows
   • Topic modeling exercises