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Duration 21 hours
Course Outline
Comprehensive training outline
- Introduction to NLP
- Concepts of NLP
- NLP Frameworks
- Business applications of NLP
- Data scraping from web sources
- Utilizing APIs to acquire text data
- Managing and storing text corpora with associated metadata
- Benefits of using Python and a quick overview of NLTK
- Practical Understanding of a Corpus and Dataset
- The necessity of a corpus
- Corpus Analysis
- Categorization of data attributes
- Various file formats for corpora
- Dataset preparation for NLP tasks
- Understanding the Structure of a Sentences
- Core NLP components
- Natural language understanding
- Morphological analysis - stems, words, tokens, speech tags
- Syntactic analysis
- Semantic analysis
- Managing ambiguity
- Text data preprocessing
- Corpus- raw text
- Sentence tokenization
- Stemming for raw text
- Lemmization of raw text
- Elimination of stop words
- Corpus-raw sentences
- Word tokenization
- Word lemmatization
- Handling Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Practical and tailored preprocessing
- Corpus- raw text
- Analyzing Text data
- Fundamental NLP features
- Parsers and parsing
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical aspects 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
- Foundations of word2vec
- Components of the word2vec model
- The underlying logic of word2vec
- Expanding the word2vec concept
- Applying the word2vec model
- Case study: Applying bag of words for automatic text summarization using simplified and true Luhn's algorithms
- Fundamental NLP features
- Document Clustering, Classification and Topic Modeling
- Document clustering and pattern discovery (hierarchical clustering, k-means, etc.)
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance
- 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: measuring sentiment intensity
- Item Response Theory
- Part of speech tagging and applications: identifying people, places, and organizations
- Advanced topic modeling: 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
- Topic modelling
Requirements
Familiarity with NLP fundamentals and an understanding of how AI is applied in business contexts.
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