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Natural Language Processing with Scala & Spark Course Online at Golearnanalytics





 
#703, 30th Main, 1st Phase, 2nd Stage,
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About The Course:-

This Course takes a practical approach to gain mastery in the area of Natural Language Processing (NLP) using Scala and Spark. With more than 85% of data generated in the world in unstructured form, the importance of NLP is growing day by day. It is very unlikely to have missed the buzz around Scala and Apache Spark in the recent days. This course combines these two extremely powerful concepts with real world examples.

Pre-requisites for this training

Knowledge of programming in any general programming language. Knowledge on functional programming is a plus. Knowledge on probability concepts is a plus although we will cover the basic mathematical concepts required in some algorithms.

Course Objectives:-

 

Introduction to Scala

  • Why functional programming suddenly matters now?
  • Core concepts behind functional programming
  • Scala basics and syntax
  • Functions and how they are evaluated
  • Higher Order Functions
  • Classes & Pattern Matching in Scala
  • Collections & Lists

Introduction to Spark

What is Apache Spark?

  • Spark Resilient Distributed Datasets (RDD)
  • Spark’s programming model
  • Manipulate data with Spark
  • Express algorithms in a functional style using Spark
  • Optimization using Spark

Natural Language Processing

  • What is Natural Language Processing (NLP)
  • Applications of NLP
  • Language Models
  • Probability Concepts required for NLP
  • Hidden Markov Models for NLP
  • Tagging using NLP
  • Naïve Parsing and Context free Parsing
  • Machine Translation
  • Word Sense Disambiguation using NLP
  • Semantics handling using NLP
  • Deep Learning Neural Networks for NLP
  • Tensor Flow for NLP

Natural Language Processing projects covered in course in a hands-on manner

  • Tagging – Domain Specific Tagging (example: Medical records)
  • Parsing – English Sentences Parsing (example: Extract part of speeches and the parse tree of English sentences)
  • Translation – Machine Translation (example: convert some Spanish word alignments to English)
  • Neural Network Deep Learning & Named Entity Recognition (example: Identify name of places, persons given in sentences)
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Start-End Dates: Contact Institute
Instructional Level: Appropriate for All
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