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

Comprehensive training curriculum

1. Introduction to NLP
   • Foundations of NLP
   • Popular NLP frameworks
   • Commercial use cases for NLP
   • Web data scraping techniques
   • Utilizing various APIs to fetch text data
   • Managing and storing text corpora with relevant metadata
   • Benefits of Python and an NLTK overview
2. Practical Understanding of a Corpus and Dataset
   • The importance of a corpus
   • Techniques for corpus analysis
   • Categorizing data attributes
   • Different file formats for corpora
   • Preparation of datasets for NLP applications
3. Understanding the Structure of a Sentence
   • Core components of NLP
   • Natural language understanding
   • Morphological analysis: stemming, word segmentation, tokenization, and speech tags
   • Syntactic analysis
   • Semantic analysis
   • Managing linguistic ambiguity
4. Text data preprocessing
   • Corpus: Raw text
     • Sentence tokenization
     • Stemming raw text
     • Lemmatization of raw text
     • Removal of stop words
   • Corpus: Raw sentences
     • Word tokenization
     • Word lemmatization
   • Handling Term-Document and Document-Term matrices
   • Tokenizing text into n-grams and sentences
   • Customized preprocessing strategies
5. Analyzing Text data
   • Fundamental NLP features
     • Parsers and parsing techniques
     • POS tagging and taggers
     • Named entity recognition
     • N-grams
     • Bag of words
   • Statistical features 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
     • Word2vec fundamentals
     • Architecture of the word2vec model
     • Logic behind the word2vec model
     • Extensions of the word2vec concept
     • 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 mining (including hierarchical clustering, k-means, etc.)
   • Document comparison and classification using TFIDF, Jaccard, and cosine distance measures
   • Document classification using Naïve Bayes and Maximum Entropy
7. Identifying Important Text Elements
   • Dimensionality reduction techniques: 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
   • Assessing sentiment intensity (positive vs. negative)
   • Item Response Theory
   • Applying Part of speech tagging to identify people, places, and organizations in text
   • Advanced topic modeling using Latent Dirichlet Allocation
9. Case studies
   • Extracting insights from unstructured user reviews
   • Sentiment classification and visualization of product review data
   • Analyzing search logs to identify usage patterns
   • Text classification
   • Topic modelling