NLP Training Using Python Including Chatbot

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Course Duration

30 Days

Training Options

Classroom / Online

nlp chatbot course

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Syllabus

Customized syllabus based on industry needs.

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Real time industry experienced trainers.

Limited

Limited students per batch.

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Training Options

Classroom Training

  • Customized curriculum as per industry needs.
  • Training by real-time professionals.
  • The best classroom and Lab infrastructure.
  • Student friendly staff and management.
  • 365 Days placement support with partner clients.

Online Training

  • Live demonstration of of features and practicals.
  • Get LMS access of each online training session that you attend through GotoMeeting.
  • Gain guidance on certification.
  • Attend a Free Demo before signing up.

NLP PART 1

  1. NLP OVERVIEW

What is importance of machine learning and NLP, Deep learning and NLP, 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 group’s 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
  • Learning to write your own grammar
  • Writing a PROBABILISTIC CFG
  • 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 Dependency grammar and projective dependency
  • Parsing a chart
  • Using in-builts NERs
  • Creating inverting 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 scene
  • Performing Sentiment analysis
  • Exploring advanced Sentiment analysis.
  • Creating a Conversational Assistant or Chatbot

NLP PART3

  • Sentiment Analysis using Deep Learning

[Content will be given later]

NLP PART4

  • Building Artificial Intelligence based Chatbots from Scratch.

[Content will be given later]

Upcoming Batch Schedules

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