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Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Core concepts of NLP
- Key NLP frameworks
- Commercial use cases for NLP
- Web scraping techniques
- Leveraging APIs to extract text data
- Managing and storing text corpora, including content and relevant metadata
- Benefits of Python and a rapid NLTK overview
- Practical Insights into Corpora and Datasets
- The necessity of corpora
- Corpus analysis methods
- Data attribute types
- File formats for corpora
- Dataset preparation for NLP projects
- Deconstructing Sentence Structure
- NLP components
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and POS tags
- Syntactic analysis
- Semantic analysis
- Addressing ambiguity
- Text Data Preprocessing
- Raw Text Corpus
- Sentence tokenization
- Stemming
- Lemmatization
- Stop word removal
- Raw Sentences Corpus
- Word tokenization
- Word lemmatization
- Utilizing Term-Document and Document-Term matrices
- Converting text into n-grams and sentences
- Customized and practical preprocessing strategies
- Raw Text Corpus
- Text Data Analysis
- Foundational NLP features
- Parsers and parsing mechanisms
- Part-of-Speech (POS) tagging and tools
- Named Entity Recognition (NER)
- N-grams
- Bag of Words
- Statistical NLP features
- Linear algebra concepts for NLP
- Probabilistic theory applications
- TF-IDF
- Vectorization techniques
- Encoders and decoders
- Normalization
- Probabilistic models
- Advanced feature engineering in NLP
- Introduction to word2vec
- word2vec model components
- Underlying logic of word2vec
- Extensions of the word2vec concept
- Implementing word2vec
- Case study: Applying Bag of Words for automated text summarization using simplified and authentic Luhn algorithms
- Foundational NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
- Comparing and classifying documents via 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 using Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Evaluating positive versus negative sentiment degrees
- Item Response Theory
- POS tagging applications: identifying people, places, and organizations in text
- Advanced topic modeling: Latent Dirichlet Allocation
- Case studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of product review data
- Extracting usage patterns from search logs
- Text classification
- Topic modeling
Requirements
Foundational knowledge of NLP principles and an understanding of how AI is applied within business contexts
21 Hours
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