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
- Major NLP frameworks
- Industry applications of NLP
- Data extraction from web sources
- Utilizing APIs for text data retrieval
- Managing and storing text corpora along with associated metadata
- Benefits of Python and a rapid introduction to NLTK
- Practical Corpus and Dataset Management
- The importance of using corpora
- Corpus analysis techniques
- Categories of data attributes
- Common file formats for corpora
- Preparing datasets for NLP solutions
- Sentence Structure Analysis
- NLP components
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and speech tags
- Syntactic analysis
- Semantic analysis
- Addressing ambiguity
- Text Data Preprocessing
- Raw Text Corpus
- Sentence tokenization
- Stemming for raw text
- Lemmatization of raw text
- Stop word removal
- Raw Sentences Corpus
- Word tokenization
- Word lemmatization
- Working with Term-Document/Document-Term matrices
- Converting text into n-grams and sentences
- Customized and practical preprocessing strategies
- Raw Text Corpus
- Analyzing Text Data
- Basic NLP features
- Parsers and parsing mechanisms
- Part-of-Speech (POS) tagging and taggers
- Named Entity Recognition (NER)
- N-grams
- Bag of Words (BoW)
- Statistical NLP features
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization techniques
- Encoders and decoders
- Normalization
- Probabilistic models
- Advanced Feature Engineering in NLP
- Word2vec fundamentals
- Architecture of the word2vec model
- Operational logic of word2vec
- Extending word2vec concepts
- Practical applications of word2vec
- Case Study: Implementing Bag of Words for automatic text summarization using simplified and standard Luhn algorithms
- Basic 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 metrics
- 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 via Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Evaluating sentiment polarity and intensity
- Item Response Theory
- Part-of-speech tagging applications: extracting people, places, and organizations from text
- Advanced topic modeling: Latent Dirichlet Allocation (LDA)
- Case Studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of product review data
- Mining search logs to identify usage patterns
- Text classification
- Topic modeling
Requirements
Familiarity with NLP principles and an understanding of how AI drives business value
21 Hours
Testimonials (1)
Individual support
