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Duration 21 hours
Course Outline
Comprehensive training curriculum
- Foundations of NLP
- Core concepts of NLP
- Major NLP frameworks
- Commercial use cases for NLP
- Web scraping for data acquisition
- Retrieving text data via various APIs
- Managing text corpora and preserving associated metadata
- Benefits of Python and an NLTK quick start
- Practical Insights into Corpora and Datasets
- The necessity of corpora
- Corpus examination
- Data attribute types
- File formats for corpora
- Preparing datasets for NLP tasks
- Analyzing Sentence Structure
- Key NLP components
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and speech tags
- Syntactic analysis
- Semantic analysis
- Managing ambiguity
- Text Data Preprocessing
- Raw text corpora
- Sentence segmentation
- Stemming raw text
- Lemmatization of raw text
- Removal of stop words
- Raw sentence corpora
- Word segmentation
- Word lemmatization
- Handling Term-Document and Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized preprocessing workflows
- Raw text corpora
- Text Data Analysis
- Essential NLP features
- Parsers and parsing techniques
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical aspects of NLP
- Linear algebra concepts for NLP
- Probability theory in NLP
- TF-IDF
- Vectorization
- Encoders and decoders
- Normalization
- Probabilistic models
- Advanced feature engineering in NLP
- Introduction to word2vec
- Components of the word2vec model
- Underlying logic of word2vec
- Extending the word2vec concept
- Applying the word2vec model
- Case study: Automatic text summarization using the Bag of Words approach with simplified and exact Luhn's algorithms
- Essential NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (e.g., hierarchical clustering, k-means)
- Document comparison and classification using TFIDF, Jaccard, and cosine similarity
- Document classification via Naïve Bayes and Maximum Entropy
- Extracting Significant Text Elements
- Dimensionality reduction: PCA, SVD, and non-negative matrix factorization
- Topic modeling and retrieval via Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Distinguishing positive vs. negative sentiment
- Item Response Theory
- Applying POS tagging to identify people, places, and organizations
- Advanced topic modeling: Latent Dirichlet Allocation
- Case Studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of product reviews
- Identifying usage patterns from search logs
- Text classification
- Topic modelling
Requirements
Familiarity with NLP fundamentals and an understanding of how AI is applied in business contexts
Testimonials (1)
Individual support
