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Duration 21 hours
Course Outline
Comprehensive course structure
- Introduction to NLP
- Fundamentals of NLP
- NLP frameworks
- Commercial uses of NLP
- Web data scraping
- Utilising various APIs to fetch text data
- Managing and storing text corpora with associated metadata
- Benefits of Python and a concise NLTK overview
- Practical Insights into Corpora and Datasets
- The necessity of a corpus
- Corpus analysis techniques
- Categorisation of data attributes
- File formats for corpora
- Preparing datasets for NLP tasks
- Sentence Structure Analysis
- Core components of NLP
- Natural language understanding
- Morphological analysis: stemming, words, tokens, and speech tags
- Syntactic analysis
- Semantic analysis
- Managing ambiguity
- Text Data Preprocessing
- Corpus: Raw text
- Sentence tokenisation
- Stemming of raw text
- Lemmatization of raw text
- Removing stop words
- Corpus: Raw sentences
- Word tokenisation
- Word lemmatization
- Manipulating Term-Document and Document-Term matrices
- Tokenising text into n-grams and sentences
- Customised preprocessing workflows
- Corpus: Raw text
- Text Data Analysis
- Essential NLP features
- Parsers and parsing techniques
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical aspects of NLP
- Linear algebra concepts for NLP
- Probabilistic theory in NLP
- TF-IDF
- Vectorization
- Encoders and decoders
- Normalisation
- Probabilistic models
- Advanced feature engineering in NLP
- Foundations of word2vec
- Architectural elements of the word2vec model
- Underlying logic of word2vec
- Extending the word2vec concept
- Applying the word2vec model
- Case study: Applying bag of words for automated text summarisation using simplified and accurate Luhn's algorithms
- Essential NLP features
- Document Clustering, Classification, and Topic Modelling
- Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
- Document comparison and classification using TF-IDF, Jaccard, and cosine similarity
- Document classification using Naïve Bayes and Maximum Entropy
- Identifying Key Text Elements
- Dimensionality reduction: PCA, SVD, and non-negative matrix factorization
- Topic modelling and information retrieval via Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modelling
- Positive vs. negative sentiment intensity
- Item Response Theory
- POS tagging applications: identifying people, places, and organisations in text
- Advanced topic modelling: Latent Dirichlet Allocation
- Case Studies
- Extracting insights from unstructured user reviews
- Sentiment classification and visualisation of product review data
- Analyzing search logs to identify usage patterns
- Text classification
- Topic modelling
Requirements
Familiarity with NLP fundamentals and an understanding of AI applications within a business context
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