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Duration 21 hours
Course Outline
Comprehensive training structure
- Introduction to NLP
- Foundations of NLP
- Key NLP frameworks
- Commercial use cases for NLP
- Web scraping techniques
- Utilizing various APIs for text data retrieval
- Managing text corpora and storing relevant metadata
- Benefits of Python and an NLTK introduction
- Practical Understanding of a Corpus and Dataset
- The role of a corpus in NLP
- Analyzing corpora
- Categorizing data attributes
- File formats for corpora
- Dataset preparation for NLP applications
- Understanding the Structure of a Sentences
- Core components of NLP
- Natural language understanding concepts
- 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
- Constructing Term-Document and Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing strategies
- Corpus: raw text
- Analyzing Text data
- Fundamental NLP features
- Parsers and parsing techniques
- Part-of-speech (POS) tagging and taggers
- Named entity recognition
- N-grams
- Bag of words model
- Statistical features in NLP
- Linear algebra concepts for NLP
- Probabilistic theory in NLP
- TF-IDF metrics
- Vectorization methods
- Encoders and Decoders
- Data normalization
- Probabilistic Models
- Advanced feature engineering and NLP
- Introduction to word2vec
- Architecture of the word2vec model
- Logical flow of the word2vec model
- Extensions of the word2vec concept
- Applying the word2vec model
- Case study: Applying bag of words for automatic text summarization using simplified and true Luhn's algorithms
- Fundamental NLP features
- Document Clustering, Classification and Topic Modeling
- Document clustering and pattern mining (hierarchical, k-means, etc.)
- Document comparison and classification using TF-IDF, Jaccard, and cosine distance
- Classification using Naïve Bayes and Maximum Entropy
- Identifying Important 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 Modeling
- Distinguishing positive and negative sentiment degrees
- Item Response Theory
- Applying POS tagging to identify people, places, and organizations
- Advanced topic modelling: Latent Dirichlet Allocation
- Case studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of product review data
- Mining search logs to identify usage patterns
- Text classification
- Topic modelling
Requirements
Familiarity with NLP principles and an understanding of AI applications in business contexts.
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