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Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Understanding NLP
- NLP Frameworks
- Commercial applications of NLP
- Web data scraping
- Retrieving text data via various APIs
- Managing and storing text corpora, including content and relevant metadata
- Benefits of using Python and an intensive crash course on NLTK
- Practical Insights into Corpus and Dataset
- Importance of having a corpus
- Corpus Analysis
- Types of data attributes
- Various file formats for corpora
- Preparing a dataset for NLP applications
- Comprehending Sentence Structure
- Core components of NLP
- Natural language understanding
- Morphological analysis: stems, words, tokens, and speech tags
- Syntactic analysis
- Semantic analysis
- Addressing ambiguity
- Preprocessing Text Data
- Corpus – raw text
- Sentence tokenization
- Stemming for raw text
- Lemmatization of raw text
- Stop word removal
- Corpus – raw sentences
- Word tokenization
- Word lemmatization
- Working with Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Practical and customized preprocessing
- Corpus – raw text
- Analyzing Text Data
- Basic features of NLP
- Parsers and parsing
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical features of NLP
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization
- Encoders and Decoders
- Normalization
- Probabilistic Models
- Advanced feature engineering and NLP
- Foundations of word2vec
- Components of the word2vec model
- Logic behind the word2vec model
- Extensions of the word2vec concept
- Application of the word2vec model
- Case study: Applying Bag of Words – automatic text summarization using simplified and authentic Luhn's algorithms
- Basic features of NLP
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, clustering, etc.)
- Comparing and classifying documents using TF-IDF, Jaccard, and cosine distance measures
- Document classification using Naïve Bayes and Maximum Entropy
- Identifying Key Text Elements
- Dimensionality reduction: Principal Component Analysis, Singular Value Decomposition, non-negative matrix factorization
- Topic modeling and information retrieval using Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Positive vs. negative: gauging sentiment degree
- Item Response Theory
- Part of speech tagging and its application: identifying people, places, and organizations mentioned in text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case Studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Mining search logs for usage patterns
- Text classification
- Topic modelling
Requirements
Knowledge and awareness of NLP principles and an appreciation of AI application in business
21 Hours
Testimonials (1)
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