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Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Understanding NLP
- NLP Frameworks
- Commercial applications of NLP
- Web data scraping
- Leveraging various APIs for text data retrieval
- 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
- Types of data attributes
- Different file formats for corpora
- Preparing datasets for NLP applications
- Understanding Sentence Structure
- Components of NLP
- Natural language understanding
- Morphological analysis - stems, words, tokens, speech tags
- Syntactic analysis
- Semantic analysis
- Handling ambiguity
- Text Data Preprocessing
- Corpus - raw text
- Sentence tokenization
- Stemming for raw text
- Lemmatization of raw text
- Stop word removal
- Corpus - raw sentences
- Word tokenization
- Word lemmatization
- Working with Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Practical and customized preprocessing
- Corpus - raw text
- Analyzing Text Data
- Basic features of NLP
- Parsers and parsing
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical features of NLP
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization
- Encoders and Decoders
- Normalization
- Probabilistic Models
- Advanced feature engineering and NLP
- Word2vec fundamentals
- Components of the word2vec model
- Logic behind the word2vec model
- Extending the word2vec concept
- Applications of the word2vec model
- Case study: Applying the bag of words model for automatic text summarization using simplified and authentic Luhn algorithms
- Basic features of NLP
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, clustering, etc.)
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance measures
- 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 using Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Positive vs. negative: degree of sentiment
- Item Response Theory
- Part of speech tagging and its application: identifying people, places, and organizations mentioned in text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case studies
- Mining unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Mining search logs for usage patterns
- Text classification
- Topic modelling
Requirements
Foundational knowledge and awareness of NLP principles, along with an understanding of how AI is applied within business contexts.
21 Hours
Testimonials (1)
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