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Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Foundations of NLP
- NLP Frameworks
- Commercial use cases for NLP
- Web data scraping
- Retrieving text data via various APIs
- Managing and storing text corpora, including content and relevant metadata
- Benefits of using Python and a crash course on NLTK
- Practical Understanding of a Corpus and Dataset
- The necessity of a corpus
- Corpus analysis
- Types of data attributes
- Various file formats for corpora
- Preparing datasets for NLP applications
- Understanding Sentence Structure
- NLP components
- Natural language understanding
- Morphological analysis: stems, words, tokens, and speech tags
- Syntactic analysis
- Semantic analysis
- Addressing ambiguity
- Text Data Preprocessing
- Corpus - Raw Text
- Sentence tokenization
- Stemming raw text
- Lemmatization of raw text
- Removing stop words
- Corpus - Raw Sentences
- Word tokenization
- Word lemmatization
- Working with Term-Document and Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing
- Corpus - Raw Text
- Analyzing Text Data
- Basic NLP Features
- Parsers and parsing
- POS tagging and taggers
- Named Entity Recognition
- N-grams
- Bag of Words
- Statistical NLP Features
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization
- Encoders and Decoders
- Normalization
- Probabilistic Models
- Advanced Feature Engineering and NLP
- Word2vec fundamentals
- Components of the word2vec model
- Logic behind the word2vec model
- Extending the word2vec concept
- Applying the word2vec model
- Case Study: Applying Bag of Words for automatic text summarization using simplified and true Luhn’s algorithms
- Basic NLP Features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance measures
- Document classification using Naïve Bayes and Maximum Entropy
- Identifying Key Text Elements
- Dimensionality reduction: Principal Component Analysis, Singular Value Decomposition, and Non-negative Matrix Factorization
- Topic modeling and information retrieval using Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Positive vs. negative: measuring sentiment degree
- Item Response Theory
- POS tagging applications: identifying people, places, and organizations in text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case Studies
- Mining unstructured user reviews
- Sentiment classification and visualization of product review data
- Analyzing search logs for usage patterns
- Text classification
- Topic modeling
Requirements
Familiarity with NLP principles and an understanding of how AI is applied in business contexts.
21 Hours
Testimonials (1)
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