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.
Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Core concepts of NLP
- Key NLP frameworks
- Commercial applications of NLP
- Web data scraping techniques
- Utilizing various APIs for text data retrieval
- Managing and storing text corpora, including content and relevant metadata
- Benefits of Python and an intensive NLTK crash course
- Practical Insights into Corpora and Datasets
- The necessity of corpora
- Corpus analysis methods
- Types of data attributes
- Common file formats for corpora
- Preparing datasets for NLP applications
- Understanding Sentence Structure
- NLP components overview
- Natural language understanding
- Morphological analysis: stems, words, tokens, and POS tags
- Syntactic analysis
- Semantic analysis
- Strategies for handling ambiguity
- Text Data Preprocessing
- Corpus: Raw text processing
- Sentence tokenization
- Stemming for raw text
- Lemmatization of raw text
- Removal of stop words
- Corpus: Raw sentences processing
- Word tokenization
- Word lemmatization
- Working with Term-Document and Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing techniques
- Corpus: Raw text processing
- Analyzing Text Data
- Fundamental NLP features
- Parsers and parsing techniques
- POS tagging and tagger tools
- Named entity recognition
- N-grams
- Bag of Words model
- Statistical features in NLP
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization
- Encoders and decoders
- Normalization
- Probabilistic models
- Advanced feature engineering and NLP
- Word2vec fundamentals
- Components of the Word2vec model
- Logic behind the Word2vec model
- Extensions of the Word2vec concept
- Practical applications of the Word2vec model
- Case Study: Applying the Bag of Words model for automatic text summarization using simplified and standard Luhn's algorithms
- Fundamental NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
- Comparing and classifying documents using TF-IDF, Jaccard, and cosine distance measures
- Document classification using Naïve Bayes and Maximum Entropy models
- Identifying Key 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
- Evaluating sentiment: positive vs. negative degrees
- Item Response Theory
- POS tagging applications: 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
- Analyzing search logs for usage patterns
- Text classification
- Topic modeling
Requirements
Familiarity with the principles of NLP and an understanding of how AI applications benefit business operations.
21 Hours
Testimonials (1)
Individual support
