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Course Outline
Comprehensive training syllabus
- Introduction to NLP
- Core concepts of NLP
- NLP Frameworks
- Commercial applications of NLP
- Web scraping techniques
- Utilizing APIs to retrieve text data
- Managing and storing text corpora, including metadata retention
- Benefits of Python with a crash course on NLTK
- Practical Corpus and Dataset Management
- The necessity of a corpus
- Corpus analysis methodologies
- Data attribute types
- Corpus file formats
- Dataset preparation for NLP use cases
- Understanding Sentence Structure
- NLP components
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and part-of-speech tags
- Syntactic analysis
- Semantic analysis
- Resolving ambiguity
- Text Data Preprocessing
- Corpus - Raw Text
- Sentence tokenization
- Stemming raw text
- Lemmatization of raw text
- Stop word removal
- Corpus - Raw Sentences
- Word tokenization
- Word lemmatization
- Handling Term-Document/Document-Term matrices
- Tokenizing text into n-grams and sentences
- Customized and practical preprocessing strategies
- Corpus - Raw Text
- Analyzing Text Data
- Basic NLP Features
- Parsers and parsing mechanisms
- Part-of-speech tagging and taggers
- Name 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 for NLP
- Foundations of word2vec
- Word2vec model components
- Logic behind the word2vec model
- Extensions of the word2vec concept
- Applying the word2vec model
- Case Study: Bag of Words application for automatic text summarization using simplified and standard Luhn's algorithms
- Basic NLP Features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (including hierarchical clustering, k-means, etc.)
- Comparing and classifying documents via TFIDF, Jaccard, and cosine distance metrics
- Document classification using Naïve Bayes and Maximum Entropy
- 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
- Sentiment degree: Positive vs. negative
- Item Response Theory
- Part-of-speech tagging applications: Identifying people, places, and organizations
- Advanced topic modeling: Latent Dirichlet Allocation
- Case Studies
- Analyzing unstructured user reviews
- Sentiment classification and visualization of product review data
- Mining search logs for usage patterns
- Text classification
- Topic modeling
Requirements
Existing knowledge and understanding of NLP principles, along with an appreciation for how AI is applied in business contexts.
21 Hours
Testimonials (1)
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