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Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Core concepts of NLP
- Key NLP frameworks
- Commercial implementations of NLP
- Web data scraping techniques
- Utilizing APIs to extract textual data
- Managing and storing text corpora, including content and relevant metadata
- Benefits of using Python and a crash course on NLTK
- Practical insights into Corpora and Datasets
- The necessity of corpora
- Corpus analysis methods
- Various data attribute types
- Different file formats for storing corpora
- Preparing datasets for NLP applications
- Understanding Sentence Structure
- NLP components
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and part-of-speech tags
- Syntactic analysis
- Semantic analysis
- Managing ambiguity
- Text Data Preprocessing
- Corpus: raw text
- Sentence tokenization
- Stemming for raw text
- Lemmatization of raw text
- Removal of stop words
- Corpus: raw sentences
- Word tokenization
- Word lemmatization
- Working with Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing strategies
- Corpus: raw text
- Analysis of Text Data
- Basic NLP features
- Parsers and parsing techniques
- Part-of-speech (POS) tagging and taggers
- Named Entity Recognition
- N-grams
- Bag of Words
- Statistical features of NLP
- Linear algebra concepts for NLP
- Probabilistic theories in NLP
- TF-IDF
- Vectorization
- Encoders and Decoders
- Normalization
- Probabilistic models
- Advanced feature engineering and NLP
- Foundations of word2vec
- Key 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 authentic Luhn's algorithms
- Basic NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (including hierarchical clustering, k-means, and other methods)
- Comparing and classifying documents using TFIDF, 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
- Sentiment degrees: positive versus negative
- Item Response Theory
- Part-of-speech tagging applications: identifying people, places, and organizations in text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case studies
- Mining unstructured user reviews
- Sentiment classification and visualization of product review data
- Analyzing search logs to identify usage patterns
- Text classification
- Topic modeling
Requirements
Familiarity with NLP principles and an understanding of how AI is applied in business contexts.
21 Hours
Testimonials (1)
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