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Duration 21 hours
Course Outline
Comprehensive training outline
- Introduction to NLP
- Grasping the fundamentals of NLP
- NLP Frameworks
- Commercial use cases of NLP
- Web data scraping
- Retrieving text data via various APIs
- Managing text corpora, including content and metadata storage
- Benefits of Python and a quick NLTK overview
- Practical Understanding of a Corpus and Dataset
- The necessity of a corpus
- Corpus Analysis
- Types of data attributes
- Various file formats for corpora
- Preparing datasets for NLP applications
- Understanding the Structure of a Sentences
- Core NLP components
- Natural language understanding
- Morphological analysis - stems, words, tokens, speech tags
- Syntactic analysis
- Semantic analysis
- Managing ambiguity
- Text data preprocessing
- Corpus: raw text
- Sentence tokenization
- Stemming raw text
- Lemmizing raw text
- Removing stop words
- Corpus: raw sentences
- Word tokenization
- Word lemmatization
- Utilizing Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Practical and tailored preprocessing
- Corpus: raw text
- Analyzing Text data
- Fundamental NLP features
- Parsers and parsing techniques
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical NLP features
- Linear algebra concepts for NLP
- Probabilistic theory in NLP
- TF-IDF
- Vectorization
- Encoders and Decoders
- Normalization
- Probabilistic Models
- Advanced feature engineering in NLP
- Foundations of word2vec
- Components of the word2vec model
- The logic behind the word2vec model
- Extending the word2vec concept
- Applying 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 Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
- Document comparison and classification using TFIDF, Jaccard, and cosine distance
- Document classification via Naïve Bayes and Maximum Entropy
- Identifying Important Text Elements
- Dimensionality reduction: PCA, SVD, and non-negative matrix factorization
- Topic modeling and information retrieval via Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis and Advanced Topic Modeling
- Positive vs. negative: measuring sentiment intensity
- Item Response Theory
- POS tagging applications: identifying people, places, and organizations in text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Extracting usage patterns from search logs
- Text classification
- Topic modelling
Requirements
Familiarity with NLP principles and an understanding of how AI is applied in business contexts.
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