Applied Natural Language Processing

Ex utamur fierent tacimates duis choro an

Lorem ipsum dolor sit amet, ius minim gubergren ad. At mei sumo sonet audiam, ad mutat elitr platonem vix. Ne nisl idque fierent vix.

Overview

Natural language processing (NLP) is one of the most important technologies of the information age.Human language can be ambiguous which is because it is related in terms difficult for a machine to fathom otherwise. Here is when NLP comes in. It breaks down the structure and extracts the relevant information.

Summarization and keyword tagging are just the first steps of the ladder where NLP can help in finding a certain entity or word in a document.

Objective

In this course, you will be given a detailed overview of NLP and how to use classic machine learning methods. You will learn about Statistical Machine Translation as well as Deep Semantic Similarity Models (DSSM) and their applications. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. Text mining, analysis, chat bot development are some of the examples of this technique.

Prerequisites

  • Linear Algebra
  • Calculus and Statistics
  • Programming and Basic Machine Learning.

What you'll learn

  • Deep Understanding of NLP
  • Getting Hands dirty on ML algorithms required in texts
  • Linguistic Features in text
  • Text Classification
  • Sentiment classification
  • Feature Representation in texts
  • Developing NER from Scratch
  • Developing word2vec embedding using both methods of word2vec
  • Chat Bot Development

Course Outline

  • History of Natural Language Processing
  • Evolution of Natural language processing
  • Traditional methods of Natural Language Processing
  • Current Trends in Natural Language Processing
  • Available libraries in Natural Language Processing
  • People to follow for updates in NLP
  • Journey from NLP->NLU->NLG
  • Getting Text in the Environment
  • Noise Removal in text
  • Identifying and cleaning data in text
  • Operations with text in python
  • Types of Regex
  • Applications of Regex
  • Hands on Regex problems
  • Linear classifier
  • Naive Bayes
  • SVM
  • Basic of Neural Network

 

  • Part of Speech Tagging
  • Dependency Parse Tree
  • Tokenization
  • Lemmatization
  • Stemming
  • Bag of Words
  • Tf-idf
  • Tokenization
  • ngrams
  • Introduction to Word Embeddings
  • Sentiment classification
  • News Article classification
  • Invoice Classification
  • Amazon Review Classification
  • What is NER?
  • Developing NER from Scratch
  • Using in built NER’s
  • Limitations of NER
  • Word embeddings
  • Word2vec
  • Glove
  • Elmo
  • BERT
  • Implementation of all word Embeddings in Classification
  • NLTK
  • Gensim
  • TextBlob
  • Spacy
  • Various other libraries
  • Building chatbots using Existing Libraries
  • Building Rule based Chatbot
  • Additional Information on chatbots
  • Research Paper discussions from Experts
  • Monthly interactions with Industry experts
  • 24*7 support for course
  • Post training – 3 months extended support
  • Live Projects/Hackathons on request
  • Course Id                                             A103