Natural Language Processing Training Course

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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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Course Overview

Natural Language Processing is the field of study that examines interactions between humans and machines. It deals primarily with enabling machines to understand human languages. The main aim of this science is to allow computers to analyze, process large amounts of data in natural languages. The course provides an innovative methodology and sample exercises that will help you go deeper into Natural Language processing. NLP is the study of Recurrent Neural networks and mathematical operations. These include vectors, matrices, and sequences to form understanding.

Course Outline


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


This course is aligned to help you gain comprehensive knowledge of the various concepts of Natural Language Processing


Upon completion of the course, you will be able to identify Natural Language Processing tasks in your day-to-day work.

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 Statistics
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


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