Natural Language Processing

Natural Language Processing or NLP is one of the most demanded technology at present. It is a field of AI which allows machines to interpret and comprehend human language. It is being used in emails, advertisements, language translations, web searches and many more.

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Natural Language Processing program overview

This course is created as an introduction to the key concepts of NLP. It is a practice-oriented course which offers practical assignments that give learners hands-on expertise with tasks such as duplicates detection, text classification, and named entities recognition.

Overview

Skills

You will learn how to build Artificial Intelligence for automation. Also, learn how to develop AI solutions.

The registration process

Once you have completed our simplified enrolment process, you’ll receive an email confirmation with your payment receipt in your registered email ID. You can then access the entire content of the online student portal immediately by logging in to your account on our site. Should you require any assistance please reach out to us via email (support@greycampus.com) or via our online chat system.

The course curriculum

The curriculum for this Natural Language Processing training incorporates all updates to the certification exam. The following is a list of broad topics covered

  • One Hot Encoding
  • Discovering relationship between Words
  • Word2Vec for word embeddings
  • Understanding Skip-Gram Model
  • Exercise: Building Word2Vec model
  • Notebook: Word2Vec model for Movie Reviews
  • Excercise: Using pre-trained Word2Vec model
  • Notebook: Sentiment Analysis using Word2Vec
  • Assignment: Building Word2Vec model on Books
  • What's Next
  • Generative Models for NLP tasks
  • Text Generation using Char-RNN
  • Exercise: Data Preparation for Char-RNN
  • Exercise: Using Batch Generator for Data Preprocessing
  • Exercise: Building and Training Char-RNN
  • Notebook: Text Generation using Char-RNN
  • Assignment: Char-RNN for Question Answering System
  • What's Next
  • Language Translation using Statstics
  • Sequence to Sequence (Seq2Seq) Model
  • Exercise: Building Seq2Seq model - Data Preprocessing
  • Seq2Seq Model : Understanding the Components
  • Exercise: Seq2Seq Model - Encoder
  • Exercise: Seq2Seq Model - Decoder
  • Seq2Seq Model - Understanding Prediction steps
  • Exercise: Seq2Seq Model - Encoder and Decoder for Prediction
  • Exercise: Seq2Seq model Prediction
  • How to use Keras for Seq2Seq model
  • Notebook: Seq2Seq model for Language Translation
  • Stacked and Bi-Directional LSTM
  • Attention in Seq2Seq Model
  • Alignment Weights in Attention
  • Attention Layer in Seq2Seq Model
  • Exercise: Implement Attention for Training Model
  • Exercise: Implement Attention for Prediction Model
  • Notebook: Using Attention in Seq2Seq Model for Language translation
  • Seq2Seq model Use cases
  • Exercise: Deploying ML Model - Prediction Module
  • Notebook: Seq2Seq Prediction Module
  • Exercise: Building a Web Service
  • Notebook: Web Service for Prediction
  • Exercise: Deploying and Testing Web Service
  • Notebook: Testing Web Service
  • Deploying ML model as Chatbot
  • Using Slack for deploying Chatbot
  • Exercise: Using Web Service as Chatbot in Slack