Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Comprehensive training curriculum
- Introduction to NLP
- Concepts in NLP
- NLP Frameworks
- Commercial use cases for NLP
- Web data scraping
- Utilizing various APIs to fetch text data
- Managing and storing text corpora, including content and associated metadata
- Benefits of Python and an NLTK introductory session
- Practical insights into Corpora and Datasets
- The necessity of a corpus
- Corpus Analysis
- Categorization of data attributes
- Various file formats for corpora
- Preparing datasets for NLP applications
- Comprehending Sentence Structure
- Core components of NLP
- 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 for raw text
- Lemmization of raw text
- Stop word removal
- Corpus: Raw sentences
- Word tokenization
- Word lemmatization
- Working with Term-Document and Document-Term matrices
- Text tokenization into n-grams and sentences
- Practical and custom preprocessing strategies
- Corpus: Raw text
- Analyzing Text data
- Fundamental features of NLP
- 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 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
- Applications of the word2vec model
- Case study: Application of bag of words: automatic text summarization using simplified and true Luhn's algorithms
- Fundamental features of NLP
- Document Clustering, Classification and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance measures
- Document classification using Naïve Bayes and Maximum Entropy
- Identifying Important Text Elements
- Dimensionality reduction: Principal Component Analysis, Singular Value Decomposition, and non-negative matrix factorization
- Topic modeling and information retrieval using Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis and Advanced Topic Modeling
- Positive vs. negative: sentiment intensity
- Item Response Theory
- Part of speech tagging and its application: identifying people, places, and organizations in text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case studies
- Mining unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Mining search logs for usage patterns
- Text classification
- Topic modelling
Requirements
Foundational knowledge of NLP principles and an understanding of AI applications in business contexts
Testimonials (1)
Individual support
