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Duration 21 hours
Course Outline
Comprehensive training outline
- Introduction to NLP
- Fundamentals of NLP
- Overview of NLP Frameworks
- Commercial applications of NLP
- Techniques for scraping data from the web
- Utilizing various APIs to acquire text data
- Managing text corpora by saving content and associated metadata
- Benefits of using Python and an introductory guide to NLTK
- Practical Understanding of a Corpus and Dataset
- The necessity of a corpus in NLP
- Methods of Corpus Analysis
- Classifying data attributes
- Varying file formats for corpora
- Preparing datasets for NLP applications
- Understanding the Structure of Sentences
- Core components of NLP
- Principles of natural language understanding
- Morphological analysis including stemming, word formation, tokenization, and speech tags
- Syntactic analysis techniques
- Semantic analysis processes
- Strategies for handling ambiguity
- Text data preprocessing
- Handling raw text corpora
- Sentence tokenization
- Stemming raw text data
- Lemmatizing raw text
- Removing stop words
- Processing raw sentence structures
- Word tokenization
- Word lemmatization
- Creating and working with Term-Document and Document-Term matrices
- Tokenizing text into n-grams and full sentences
- Implementing practical and customized preprocessing workflows
- Handling raw text corpora
- Analyzing Text data
- Foundational NLP features
- Concepts of parsers and parsing
- Part-of-speech (POS) tagging and taggers
- Named entity recognition
- Utilizing n-grams
- The Bag of Words model
- Statistical approaches in NLP
- Linear algebra concepts applied to NLP
- Probabilistic theory in NLP
- TF-IDF metrics
- Data vectorization techniques
- Use of Encoders and Decoders
- Data normalization methods
- Probabilistic Models
- Advanced feature engineering in NLP
- Foundations of word2vec
- Internal components of the word2vec model
- Underlying logic of 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 true Luhn's algorithms
- Foundational NLP features
- Document Clustering, Classification and Topic Modeling
- Document clustering and pattern mining techniques (including hierarchical clustering and k-means)
- 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 via 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
- Distinguishing positive vs. negative sentiment degrees
- Item Response Theory
- Applying part of speech tagging to identify people, places, and organizations in text
- Advanced topic modeling using Latent Dirichlet Allocation
- Case studies
- Mining unstructured user reviews
- Sentiment classification and visualization of product review data
- Extracting usage patterns from search logs
- Text classification projects
- Topic modelling exercises
Requirements
A solid understanding of NLP principles and an awareness of how AI applications benefit business operations.
Testimonials (1)
Individual support
