NLP Online Training

NLP ( Natural Language Processing ) Online Training

NLP Part -1

NLP Overview

What is importance of Machine Learning and NLP , and Deep Learning and NLP, and Reinforcement Learning with NLP combinations

  • Use Python nltk , SpaCy and scikit-learn to build your nlp tool set
  • Reading a simple natural language File into memory
  • split the text into individual words with regular expressions
  • Converting words into list of lowercase tokens
  • Removing uncommon words and stop words
  • Use An Open source dataset and what is the Enron dataset
  • loading the Enron dataset into memory
  • Tokenization , Lemmatization and stopword removal
  • Bag of words feature extraction with scikit-learn
  • Basic spam classification with nltk’s NaiveBayes
  • understanding origin and features of the movie review dataset
  • loading and cleaning the review dataset
  • preprocessing the dataset to remove unwanted words and characters 
  • creating tf-idf weighted natural language features
  • basic sentiment analysis with logistic regression model
  • Deep dive into raw tokons from the movie reviews
  • Advanced cleaning of tokens using python String functions and regex
  • creating n-gram features using scikit-learn
  • Experimenting with advanced scikit-learn models using nltk wrapper
  • Building a voting model with scikit-learn
  • Understanding the Origin and features of the 20 news groups dataset
  • loading the news group data, and extracting features
  • Building a document classification pipeline
  • creating a performance report of the model on the test set
  • Finding optimal hyper parameters using grid search
  • Building a text preprocessing pipeline with nltk
  • creating hashing based features from natural language
  • classify documents into 20 topics with LSA
  • Document classification with tf-idf and SVM

NLP Part -2

  • Exploring the in-built tagger
  • writing your own tagger
  • training your own tagger
  • leaning to write your own grammar
  • writing a recursive CFG
  • Using the built in Chunker
  • Writing your own simple chunker
  • training a chuncker
  • Parsing a recursive Descent
  • Parsing shift-reduce
  • Parsing Dependancy grammer and projective dependancy
  • Parsing a chart
  • Using in-builts NERs
  • creating inversing and using dictionaries
  • choosing the feature set
  • segmenting sentences using classification
  • Writing a POS tagger with Context
  • Creating an NLP pipeline
  • Solving the text similarity problem
  • Resolving Anaphora
  • Disambiguating Word sence
  • performing Sentiment analysis
  • Exploring advanced Sentiment analysis
  • Creating a Conversational Assistant or Chatbot

NLP Part – 3

  • Sentiment Analysis using Deep Learning  .. and more

NLP Part – 4

  • Building Artificial Intelligence based Chatbots from Scratch.. and more

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