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Duration 21 hours
Course Outline
Comprehensive training outline
- Introduction to NLP
- Core concepts of NLP
- Overview of NLP frameworks
- Commercial use cases for NLP
- Web scraping techniques
- Utilizing APIs to fetch text data
- Managing and storing text corpora, including content and metadata
- Benefits of using Python and an NLTK introductory session
- Practical Insight into Corpora and Datasets
- The necessity of a corpus
- Techniques for corpus analysis
- Categorizing data attributes
- File formats commonly used for corpora
- Dataset preparation strategies for NLP tasks
- Sentence Structure Analysis
- Key components of NLP
- Mechanisms of natural language understanding
- Morphological analysis: stems, words, tokens, and speech tags
- Syntactic parsing
- Semantic interpretation
- Strategies for handling ambiguity
- Text Data Preprocessing
- Corpus: Raw text processing
- Sentence-level tokenization
- Stemming raw text
- Lemmatization of raw text
- Removal of stop words
- Corpus: Raw sentences
- Word-level tokenization
- Word lemmatization
- Constructing Term-Document and Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing workflows
- Corpus: Raw text processing
- Analyzing Text Data
- Fundamental NLP features
- Parsers and the parsing process
- POS tagging and tagging tools
- Named entity recognition
- Usage of n-grams
- The Bag of Words model
- Statistical aspects of NLP
- Linear algebra concepts applied to NLP
- Probabilistic theories in NLP
- TF-IDF weighting
- Vectorization techniques
- Encoders and decoders
- Data normalization
- Probabilistic models
- Advanced feature engineering in NLP
- Introduction to word2vec
- Architectural components of the word2vec model
- Underlying logic of word2vec
- Extensions of the word2vec concept
- Practical applications of the word2vec model
- Case Study: Bag of Words application: automatic text summarization using simplified and true Luhn's algorithms
- Fundamental NLP features
- Document Clustering, Classification, and Topic Modelling
- Document clustering and pattern mining (e.g., hierarchical clustering, k-means)
- Comparing and classifying documents using TFIDF, Jaccard similarity, and cosine distance
- 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 modelling and information retrieval via Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modelling
- Distinguishing positive vs. negative sentiment intensity
- Item Response Theory
- Part of speech tagging applications: identifying people, places, and organizations
- Advanced topic modelling: Latent Dirichlet Allocation
- Case Studies
- Mining unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Extracting usage patterns from search logs
- Text classification projects
- Topic modelling exercises
Requirements
Familiarity with NLP principles and an understanding of AI applications in business contexts
Testimonials (1)
Individual support
