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Course Outline
Comprehensive training outline
- Introduction to NLP
- Core concepts of NLP
- Key NLP frameworks
- Business and commercial applications of NLP
- Data extraction from web sources
- Utilizing various APIs to access text data
- Managing and storing text corpora, including relevant metadata
- Benefits of Python and a crash course in NLTK
- Practical Insights into Corpora and Datasets
- The necessity of using corpora
- Corpus analysis techniques
- Categories of data attributes
- Common file formats for corpora
- Preparing datasets for NLP applications
- Understanding Sentence Structure
- Essential components of NLP
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and part-of-speech tags
- Syntactic analysis
- Semantic analysis
- Strategies for handling ambiguity
- Text Data Preprocessing
- Processing raw text corpora
- Sentence tokenization
- Stemming applied to raw text
- Lemmatization of raw text
- Removal of stop words
- Processing raw sentence corpora
- Word tokenization
- Word lemmatization
- Working with Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing workflows
- Processing raw text corpora
- Analyzing Text Data
- Fundamental NLP features
- Parsing and parsers
- POS tagging and tagger tools
- Named entity recognition
- N-grams
- Bag of words model
- Statistical features in NLP
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization techniques
- Encoders and decoders
- Normalization methods
- Probabilistic models
- Advanced Feature Engineering in NLP
- Introduction to Word2Vec
- Components of the Word2Vec model
- Underlying logic of Word2Vec
- Extensions of the Word2Vec concept
- Practical applications of Word2Vec
- Case Study: Applying the Bag of Words Model for Automatic Text Summarization using Simplified and Traditional Luhn's Algorithms
- Fundamental NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (including hierarchical clustering, k-means, and other clustering methods)
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance metrics
- Document classification using Naïve Bayes and Maximum Entropy models
- Identifying Key Text Elements
- Dimensionality reduction techniques: 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
- Evaluating sentiment polarity and intensity (positive vs. negative)
- Item Response Theory
- Part-of-speech tagging and its application in identifying people, locations, and organizations within text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case Studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Analyzing search logs to uncover usage patterns
- Text classification tasks
- Topic modeling exercises
Requirements
Familiarity with the fundamental principles of NLP and an understanding of how AI applications benefit business operations.
21 Hours
Testimonials (1)
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