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.
Course Outline
Comprehensive training syllabus
- Foundations of NLP
- Core concepts of NLP
- Overview of NLP frameworks
- Real-world commercial applications
- Data acquisition techniques for web scraping
- Leveraging APIs for text data retrieval
- Managing and storing text corpora along with essential metadata
- Advantages of Python and an intensive NLTK crash course
- Practical Corpus and Dataset Management
- The necessity of corpora
- Corpus analysis methodologies
- Data attribute types
- File formats for corpora
- Preparing datasets for NLP tasks
- Understanding Sentence Structure
- NLP components
- Natural language comprehension
- Morphological analysis: stems, words, tokens, and POS tags
- Syntactic analysis
- Semantic analysis
- Addressing ambiguity
- Text Data Preprocessing
- Corpus - Raw Text
- Sentence tokenization
- Stemming for raw text
- Lemmatization of raw text
- Stop word removal
- Corpus - Raw Sentences
- Word tokenization
- Word lemmatization
- Working with 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 techniques
- POS tagging and taggers
- Named entity recognition
- N-grams
- Bag of words
- Statistical features of NLP
- Linear algebra concepts for NLP
- Probabilistic theory for NLP
- TF-IDF
- Vectorization
- Encoders and Decoders
- Normalization
- Probabilistic Models
- Advanced Feature Engineering and NLP
- word2vec fundamentals
- Components of the word2vec model
- Logic behind the word2vec model
- Extensions of word2vec
- Application of the word2vec model
- Case Study: Applying Bag of Words for Automatic Text Summarization using Simplified and True Luhn's Algorithms
- Basic NLP features
- Document Clustering, Classification, and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, etc.)
- Comparing and classifying documents using 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
- Positive vs. negative sentiment degrees
- Item Response Theory
- POS 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
Familiarity with NLP principles and an understanding of how AI applications drive business value.
21 Hours
Testimonials (1)
Individual support
