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 outline
- Introduction to NLP
- Grasping the basics of NLP
- Overview of NLP Frameworks
- Commercial uses of NLP
- Extracting data from the web
- Interacting with various APIs to fetch text data
- Managing and storing text corpora, including content and relevant metadata
- Benefits of Python and an NLTK crash course
- Practical Insights into Corpora and Datasets
- The necessity of a corpus
- Analyzing a corpus
- Categories of data attributes
- Various file formats for corpora
- Preparation of 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
- Raw text corpora
- Sentence tokenization
- Stemming raw text
- Lemmatizing raw text
- Removing stop words
- Raw sentence corpora
- Word tokenization
- Word lemmatization
- Handling Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing
- Raw text corpora
- Text Data Analysis
- Fundamental NLP features
- 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 in NLP
- TF-IDF
- Vectorization
- Encoders and Decoders
- Normalization
- Probabilistic Models
- Advanced feature engineering in NLP
- Word2vec fundamentals
- Components of the word2vec model
- Underlying logic of the word2vec model
- Extending the word2vec concept
- Applications of the word2vec model
- Case study: Applying bag of words for 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.)
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance metrics
- 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 via Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Positive vs. negative: measuring sentiment intensity
- Item Response Theory
- Applying part-of-speech tagging to identify people, places, and organizations
- 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 modeling
Requirements
Familiarity with NLP principles and an understanding of how AI is applied in business contexts.
Testimonials (1)
Individual support
