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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