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Duration 21 hours
Course Outline
Detailed training outline
- Introduction to NLP
- Fundamentals of NLP
- Overview of NLP Frameworks
- Commercial use cases for NLP
- Web scraping techniques
- Retrieving text data via various APIs
- Managing text corpora: saving content and associated metadata
- Benefits of using Python with an NLTK crash course
- Practical Understanding of a Corpus and Dataset
- The necessity of a corpus
- Corpus Analysis methods
- Categorization of data attributes
- File formats suitable for corpora
- Preparing datasets for NLP applications
- Understanding the Structure of Sentences
- Core components of NLP
- Natural language understanding concepts
- Morphological analysis: stems, words, tokens, and speech tags
- Syntactic analysis
- Semantic analysis
- Strategies for handling ambiguity
- Text data preprocessing
- Corpus- raw text
- Sentence tokenization
- Stemming raw text
- Lemmization of raw text
- Removal of stop words
- Corpus-raw sentences
- Word tokenization
- Word lemmatization
- Creating Term-Document and Document-Term matrices
- Tokenizing text into n-grams and sentences
- Implementing practical, customized preprocessing workflows
- Corpus- raw text
- Analyzing Text data
- Fundamental NLP features
- Parsers and the parsing process
- Part-of-speech (POS) tagging and taggers
- Named entity recognition
- N-grams
- Bag of words model
- Statistical NLP features
- Linear algebra concepts for NLP
- Probabilistic theory in NLP
- TF-IDF weighting
- Vectorization techniques
- Encoders and Decoders
- Data normalization
- Probabilistic Models
- Advanced feature engineering in NLP
- Foundations of word2vec
- Architectural components of the word2vec model
- Internal logic of the word2vec model
- Extensions of the word2vec concept
- Practical applications of the word2vec model
- Case study: Bag of words application: 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.)
- Document comparison and classification using TFIDF, Jaccard, and cosine similarity
- Document classification using Naïve Bayes and Maximum Entropy
- Identifying Important 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
- Positive vs. negative sentiment degrees
- Item Response Theory
- Part of speech tagging applications: identifying people, places, and organizations in text
- Advanced topic modeling using Latent Dirichlet Allocation
- Case studies
- Extracting insights from unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Analyzing search logs for usage patterns
- Text classification projects
- Topic modelling exercises
Requirements
Familiarity with NLP principles and an understanding of how AI is applied in business contexts.
Testimonials (1)
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