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Duration 21 hours
Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Foundations of NLP
- Overview of NLP Frameworks
- Commercial use cases for NLP
- Web data scraping techniques
- Utilizing various APIs to fetch text data
- Managing text corpora: storing content and associated metadata
- Benefits of Python and an NLTK primer
- Practical Insights into Corpora and Datasets
- The importance of maintaining a corpus
- Methods of corpus analysis
- Identifying data attribute types
- Common file formats for text corpora
- Preparing datasets for NLP tasks
- Deconstructing Sentence Structure
- Core components of NLP
- Principles of natural language understanding
- Morphological analysis: stems, words, tokens, and speech tags
- Syntactic analysis techniques
- Semantic analysis methods
- Strategies for managing linguistic ambiguity
- Text Data Preprocessing
- Handling raw text in corpora
- Sentence-level tokenization
- Stemming applied to raw text
- Lemmatization of raw text
- Removing stop words
- Processing raw sentences in corpora
- Word-level tokenization
- Word lemmatization
- Managing Term-Document and Document-Term matrices
- Tokenizing text into n-grams and sentence structures
- Customized and practical preprocessing workflows
- Handling raw text in corpora
- Text Data Analysis
- Essential NLP features
- Parsers and parsing mechanisms
- POS tagging and tagger usage
- Named entity recognition
- Working with n-grams
- The Bag of Words model
- Statistical aspects of NLP
- Linear algebra concepts for NLP
- Probabilistic theories in NLP
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Vectorization techniques
- Encoders and Decoders
- Data normalization
- Probabilistic modeling
- Advanced feature engineering in NLP
- Word2Vec fundamentals
- Structural components of the Word2Vec model
- Operational logic of Word2Vec
- Extensions of the Word2Vec concept
- Practical applications of Word2Vec
- Case study: Bag of Words application for automatic text summarization using simplified and true Luhn's algorithms
- Essential NLP features
- Document Clustering, Classification, and Topic Modeling
- Clustering documents and mining patterns (including hierarchical clustering and k-means)
- Comparing and classifying documents using TF-IDF, Jaccard, and cosine similarity metrics
- Document classification via Naïve Bayes and Maximum Entropy models
- Identifying Key Text Elements
- Dimensionality reduction techniques: 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
- Assessing positive vs. negative sentiment intensity
- Item Response Theory
- Applying POS tagging to identify people, places, and organizations
- Advanced topic modeling with Latent Dirichlet Allocation
- Case Studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of product review data
- Mining search logs to identify usage patterns
- Text classification exercises
- Topic modeling projects
Requirements
Familiarity with NLP fundamentals and an understanding of AI applications in the business context
Testimonials (1)
Individual support
