Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Comprehensive Training Curriculum
- Introduction to Natural Language Processing
- Overview of NLP principles
- Architectural frameworks for NLP
- Commercial deployment of NLP systems
- Methodologies for web-based data acquisition
- Integration with APIs for text data retrieval
- Management of text corpora, including content and metadata preservation
- Advantages of utilizing Python and an introduction to NLTK
- Practical Foundations of Corpora and Datasets
- Necessity of maintaining structured corpora
- Analytical approaches to corpora
- Classification of data attributes
- Standard file formats for corpus storage
- Data preparation protocols for NLP applications
- Analyzing Sentence Structure and Linguistic Components
- Core components of NLP infrastructure
- Techniques for natural language understanding
- Morphological analysis, including stemming, tokenization, and speech tagging
- Principles of syntactic analysis
- Methods of semantic analysis
- Strategies for resolving linguistic ambiguity
- Text Data Preprocessing Protocols
- Processing raw text corpora
- Sentence-level tokenization techniques
- Application of stemming to raw text
- Implementation of lemmatization
- Removal of stop words
- Processing sentence-level corpora
- Word-level tokenization methods
- Word-level lemmatization procedures
- Construction of Term-Document and Document-Term matrices
- Tokenization into n-grams and sentence segments
- Customized preprocessing strategies for specific operational needs
- Processing raw text corpora
- Text Data Analysis Techniques
- Foundational NLP features
- Parsing mechanisms and syntactic parsers
- Part-of-speech tagging methodologies
- Named Entity Recognition (NER) systems
- N-gram analysis
- Bag-of-words representation
- Statistical methodologies in NLP
- Linear algebra concepts applied to NLP
- Probabilistic theories relevant to text analysis
- Implementation of TF-IDF weighting
- Text vectorization techniques
- Encoder and decoder architectures
- Data normalization procedures
- Development of probabilistic models
- Advanced Feature Engineering
- Fundamentals of word2vec
- Structural components of the word2vec model
- Operational logic of word embeddings
- Extensions of word2vec concepts
- Practical applications of word2vec models
- Case Study: Bag-of-Words Application in Text Summarization using Luhn’s Algorithms
- Foundational NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern identification (e.g., hierarchical, k-means)
- Document comparison using TFIDF, Jaccard, and cosine similarity metrics
- Document classification via Naïve Bayes and Maximum Entropy methods
- Identification of Key Textual Elements
- Dimensionality reduction techniques: PCA, SVD, and Non-negative Matrix Factorization
- Topic modeling and information retrieval via Latent Semantic Analysis
- Entity Extraction, Sentiment Analysis, and Advanced Topic Modeling
- Sentiment polarity and intensity assessment
- Item Response Theory applications
- Application of POS tagging for identifying entities (persons, locations, organizations)
- Advanced topic modeling using Latent Dirichlet Allocation (LDA)
- Operational Case Studies
- Extraction of insights from unstructured user feedback
- Sentiment classification and visualization of product review data
- Analysis of search logs for usage pattern identification
- Implementation of text classification systems
- Execution of topic modeling projects
Requirements
Demonstrated proficiency in fundamental Natural Language Processing principles and a working understanding of Artificial Intelligence applications within organizational contexts.
Testimonials (1)
Individual support
