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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 for data collection
- Utilizing APIs to fetch text data
- Managing and storing text corpora, including content and metadata
- Benefits of Python and a rapid introduction to NLTK
- Practical Insights into Corpora and Datasets
- The necessity of corpora
- Corpus analysis methods
- Categories of data attributes
- File formats suitable for corpora
- Preparing datasets for NLP projects
- Decoding Sentence Structure
- Elements of NLP
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and part-of-speech tags
- Syntactic analysis
- Semantic analysis
- Managing ambiguity
- Text Data Preprocessing
- Processing raw text corpora
- Sentence tokenization
- Stemming raw text
- Lemmatization of raw text
- Removing stop words
- Processing raw sentence corpora
- Word tokenization
- Word lemmatization
- Handling Term-Document and Document-Term matrices
- Converting text into n-grams and sentences
- Customized and practical preprocessing strategies
- Processing raw text corpora
- Analyzing Text Data
- Foundational NLP features
- Parsers and parsing techniques
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical features in NLP
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization
- Encoders and decoders
- Normalization
- Probabilistic models
- Advanced Feature Engineering and NLP
- Fundamentals of word2vec
- Components of the word2vec model
- Underlying logic of the word2vec model
- Extensions of the word2vec concept
- Applications of the word2vec model
- Case Study: Applying Bag of Words for Automatic Text Summarization using simplified and standard Luhn's algorithms
- Foundational NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (e.g., hierarchical clustering, k-means, clustering)
- Comparing and classifying documents using TFIDF, Jaccard, and cosine distance metrics
- Document classification using Naïve Bayes and Maximum Entropy methods
- 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
- Sentiment polarity: positive vs. negative degrees
- 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 for usage patterns
- 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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