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Detailed training outline
Introduction to NLP
Understanding NLP
NLP Frameworks
Commercial applications of NLP
Scraping data from the web
Working with various APIs to retrieve text data
Working and storing text corpora saving content and relevant metadata
Advantages of using Python and NLTK crash course
Practical Understanding of a Corpus and Dataset
Why do we need a corpus?
Corpus Analysis
Types of data attributes
Different file formats for corpora
Preparing a dataset for NLP applications
Understanding the Structure of a Sentences
Components of NLP
Natural language understanding
Morphological analysis - stem, word, token, speech tags
Syntactic analysis
Semantic analysis
Handling ambigiuty
Text data preprocessing
Corpus- raw text
Sentence tokenization
Stemming for raw text
Lemmization of raw text
Stop word removal
Corpus-raw sentences
Word tokenization
Word lemmatization
Working with Term-Document/Document-Term matrices
Text tokenization into n-grams and sentences
Practical and customized preprocessing
Analyzing Text data
Basic feature of NLP
Parsers and parsing
POS tagging and taggers
Name entity recognition
N-grams
Bag of words
Statistical features of NLP
Concepts of Linear algebra for NLP
Probabilistic theory for NLP
TF-IDF
Vectorization
Encoders and Decoders
Normalization
Probabilistic Models
Advanced feature engineering and NLP
Basics of word2vec
Components of word2vec model
Logic of the word2vec model
Extension of the word2vec concept
Application of word2vec model
Case study: Application of bag of words: automatic text summarization using simplified and true Luhn's algorithms
Document Clustering, Classification and Topic Modeling
Document clustering and pattern mining (hierarchical clustering, k-means, clustering, etc.)
Comparing and classifying documents using TFIDF, Jaccard and cosine distance measures
Document classifcication using Na?ve Bayes and Maximum Entropy
Identifying Important Text Elements
Reducing dimensionality: 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
Positive vs. negative: degree of sentiment
Item Response Theory
Part of speech tagging and its application: finding people, places and organizations mentioned in text
Advanced topic modeling: Latent Dirichlet Allocation
Case studies
Mining unstructured user reviews
Sentiment classification and visualization of Product Review Data
Mining search logs for usage patterns
Text classification
Topic modelling |
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