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Duration 21 hours
Course Outline
Comprehensive training outline
- Introduction to NLP
- Foundations of NLP
- Overview of NLP frameworks
- Commercial use cases for NLP
- Web data scraping techniques
- Retrieving text data via various APIs
- Managing and storing text corpora with associated metadata
- Benefits of Python and a rapid introduction to NLTK
- Practical Understanding of Corpora and Datasets
- The role of a corpus in NLP
- Techniques for corpus analysis
- Classification of data attributes
- Various file formats for storing corpora
- Dataset preparation for NLP tasks
- Analyzing Sentence Structure
- Core components of NLP
- Principles of natural language understanding
- Morphological analysis: stemming, words, tokens, and speech tags
- Syntactic analysis methods
- Semantic analysis approaches
- Strategies for handling ambiguity
- Text Data Preprocessing
- Processing raw text corpora
- Sentence-level tokenization
- Stemming raw text
- Lemmatisation of raw text
- Removal of stop words
- Processing raw sentence corpora
- Word-level tokenization
- Word lemmatisation
- Utilising Term-Document and Document-Term matrices
- Tokenising text into n-grams and sentences
- Customised and practical preprocessing strategies
- Processing raw text corpora
- Analyzing Text Data
- Foundational NLP features
- Parsers and parsing techniques
- POS tagging and tagger implementations
- Named entity recognition
- Handling n-grams
- Bag of words representation
- Statistical aspects of NLP
- Linear algebra concepts relevant to NLP
- Probabilistic theories in NLP
- TF-IDF weighting
- Vectorisation techniques
- Encoders and decoders
- Normalisation procedures
- Probabilistic modelling
- Advanced feature engineering in NLP
- Introductory concepts of word2vec
- Architectural components of the word2vec model
- Underlying logic of word2vec
- Extensions of the word2vec concept
- Practical applications of the word2vec model
- Case study: Applying bag of words for automatic text summarization using simplified and true Luhn's algorithms
- Foundational NLP features
- Document Clustering, Classification, and Topic Modelling
- Document clustering and pattern mining (including hierarchical and k-means clustering)
- Comparing and classifying documents using TF-IDF, Jaccard, and cosine distance metrics
- Document classification via Naïve Bayes and Maximum Entropy
- Identifying Key Text Elements
- Dimensionality reduction: PCA, 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
- Assessing positive vs. negative sentiment intensity
- Item Response Theory
- POS tagging applications: identifying people, places, and organisations in text
- Advanced topic modelling using Latent Dirichlet Allocation
- Case Studies
- Extracting insights from unstructured user reviews
- Sentiment classification and visualisation of product review data
- Mining search logs to identify usage patterns
- Text classification projects
- Topic modelling exercises
Requirements
A solid grasp of NLP fundamentals and an understanding of how AI can be applied in business contexts
Testimonials (1)
Individual support
