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

Comprehensive training outline

1. Introduction to NLP
   • Foundations of NLP
   • Overview of NLP Frameworks
   • Commercial use cases of NLP
   • Techniques for scraping data from the web
   • Utilizing various APIs to collect text data
   • Managing and storing text corpora, including content and associated metadata
   • Benefits of Python and an NLTK introduction
2. Practical Understanding of a Corpus and Dataset
   • The necessity of a corpus
   • Methods for Corpus Analysis
   • Categories of data attributes
   • Variations in file formats for corpora
   • Preparatory steps for datasets in NLP applications
3. Understanding the Structure of a Sentences
   • Core components of NLP
   • Principles of natural language understanding
   • Morphological analysis: stems, words, tokens, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Strategies for handling ambiguity
4. Text data preprocessing
   • Corpus: raw text
     • Sentence-level tokenization
     • Stemming techniques for raw text
     • Lemmatization of raw text
     • Elimination of stop words
   • Corpus: raw sentences
     • Word-level tokenization
     • Word-level lemmatization
   • Manipulating Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized and practical preprocessing workflows
5. Analyzing Text data
   • Fundamental NLP features
     • Parsers and parsing mechanisms
     • POS tagging and tagger implementation
     • Name entity recognition
     • Working with N-grams
     • The Bag of Words model
   • Statistical aspects of NLP
     • Linear algebra concepts applied to NLP
     • Probabilistic theory in NLP
     • TF-IDF weighting
     • Vectorization techniques
     • Use of Encoders and Decoders
     • Data normalization
     • Application of Probabilistic Models
   • Advanced feature engineering and NLP
     • Introduction to word2vec
     • Internal components of the word2vec model
     • Underlying logic of the word2vec model
     • Extensions of the word2vec concept
     • Practical applications of the word2vec model
   • Case study: Applying the Bag of Words model for automatic text summarization using simplified and full Luhn's algorithms
6. Document Clustering, Classification and Topic Modeling
   • Document clustering and pattern detection (including hierarchical clustering, k-means, and other methods)
   • Document comparison and classification using TFIDF, Jaccard index, and cosine similarity
   • Classifying documents with Naïve Bayes and Maximum Entropy models
7. Identifying Important 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
   • Distinguishing positive vs. negative sentiment and intensity
   • Item Response Theory
   • Applying part-of-speech tagging to identify people, places, and organizations in text
   • Advanced topic modeling techniques: Latent Dirichlet Allocation
9. Case studies
   • Extracting insights from unstructured user reviews
   • Classifying and visualizing sentiment in product review data
   • Analyzing search logs for usage patterns
   • Implementing text classification
   • Performing topic modelling