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    Practical Natural Language Processing - Go From Zero To Hero

    Posted By: ELK1nG
    Practical Natural Language Processing - Go From Zero To Hero

    Practical Natural Language Processing - Go From Zero To Hero
    Published 2/2023
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 7.86 GB | Duration: 20h 0m

    Learn Natural Language Processing (NLP) Creating Chatbot ,RASA, ChatGPT , BERT, Transformers, Hugging Face , Open AI GPT

    What you'll learn

    Text Preprocessing using NLTK and Spacy

    How to work on NLP pipeline

    Perform Tokenization

    Stemming & Lemmatization

    Apply Word Embeddings

    NLP Pipeline for various tasks

    Named Entity Recognition

    Text Summarization

    Building an Enterprise Grade Chatbot with Dialogflow

    Building a project on Twitter Tweets

    Build Chatbot with RASA with Advanced Integration

    Deep Learning for Sequence Data

    Transformer NLP Architecture

    ChatGPT

    BERT Model

    Hugging Face Transformers

    Requirements

    Access to Google Colab/Jupyter Notebook

    Basic to Intermediate Python Programming skills

    Optional – GCP free trial account

    Description

    Practical Natural Language Processing - Go form Zero to HeroNatural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As its name suggests, NLP is about developing techniques to process and analyze large amounts of natural language data.NLP is an important field because it helps us to better understand human communication. By developing algorithms that can automatically process and analyze language data, we can gain insights that would not be possible through manual methods. Additionally, NLP can be used to build applications that humans can interact with more easily and efficiently, such as chatbots and voice-activated assistants.There are many benefits to learning natural language processing. Here are just a few:NLP can help you to better understand human communication.NLP can be used to build applications that humans can interact with more easily and efficiently.NLP can help you to automate tedious tasks such as information extraction from unstructured text data.NLP can improve the usability of search engines and other information retrieval systems.Learning NLP can open up career opportunities in a variety of industries, including software development, data science, and marketing.NLP is an interdisciplinary field, which means that it draws on knowledge from a variety of disciplines, including linguistics, computer science, artificial intelligence, and psychology.NLP is a rapidly growing field with exciting new research being published all the time.We have designed this course such a way that, as a practitioner you will learn the core topics described below:A Collection of Important Sections to help you understand the uniqueness of Text data and the methods to process it:In the Learning Journey, you will the Important Topics in Text Processing like :Text PreprocessingWorking on NLP pipelineTokenizationStemmingLemmatizationWord EmbeddingsNLP Pipeline for various tasksNamed Entity RecognitionText SummarizationBuilding an Enterprise Grade Chatbot with Dialogflow :In this section, you will build an Enterprise Grade Chatbot using the Widely used Platform Google Cloud Platform service - DialogFlow. In the course of the journey, you will learn how to build the chatbot from scratch, and get the advantage of the Advanced Machine Learning models of Google, and use it with few clicks, and ready to implement for your own projects.Building a project on Twitter Tweets:In the Hands On Project of this section, you will learn about working with Social Media Platform - Twitter, learn how to make use of Tweepy library , perform data extraction, data mining, data preprocessing on text data, and then create World Cloud on the Basis of Tweets created on Realtime. Its a End to End Project.Build Chatbot with RASA with Advanced Integration:Rasa is an open-source chatbot framework that helps businesses build contextual assistants. It is a set of tools that enables businesses to build, train, and deploy AI-powered chatbots. With Rasa, businesses can provide their customers with engaging and personalized experiences at scale.  In this course, you will learn about its Business Use case , its implementation from scratch and integration with Slack channel so that you can start using on your projects. The chatbot can also retrieve the News from New York Times website that can answer as per the user request.Deep Learning for Sequence Data:Apart from the ML aspects, we are also going to consider the Deep Learning Neural Networks to work with text data. Recently, the progress of NLP research on text classification has arrived at the state-of-the-art (SOTA). It has achieved terrific results, showing Deep Learning methods as the cutting-edge technology to perform such tasks.  As part for your learning journey, you will learn about the Recurrent Neural Networks, LSTM Neural Networks and Attention Mechanism for Encoder-Decoder Architecture.Transformer NLP Architecture:Transformer NLP is a type of NLP that uses a deep learning approach to solve natural language tasks. This technology has revolutionized the way businesses process and analyze language-based data, making it easier than ever before to extract meaningful insights from large amounts of text. Let's take a look at how Transformer NLP works and how it can be used in the business world. ChatGPT:ChatGPT is a revolutionary new AI technology that can help businesses save time and money. It stands for “Chatbot Generated Processes and Tasks”, and it uses natural language processing (NLP) to automate mundane business tasks such as customer support, onboarding, training, sales and marketing. I You will learn the intuition behind the ChatGPT in this course.BERT Model:BERT stands for Bidirectional Encoder Representations from Transformers. It is a type of artificial intelligence (AI) designed to understand natural language better than ever before. It can be used for tasks such as sentiment analysis, question-answering, and text summarization. The technology was created by Google AI researchers who wanted to create a more robust system for understanding human language. You will explore in this course about the core Architecture of BERT in this sectionHugging Face Transformers:Hugging Face transformers is a platform that provides the community with APIs to access and use state-of-the-art pre-trained models available from the Hugging Face hub. In the Advanced Modules of this course, you will learn how to implement the State of the Art Models from the Hugging Face Hub, and implement it on the Hands On manner.

    Overview

    Section 1: Introduction to Natural Language Processing

    Lecture 1 Why NLP and how its different from Normal ML ?

    Lecture 2 Understanding Human Language

    Lecture 3 Challenges of NLP

    Lecture 4 Summary

    Section 2: Pipeline of NLP

    Lecture 5 NLP Pipeline

    Lecture 6 Data Extraction and Text Cleaning hands On

    Lecture 7 Introduction to NLTK library

    Lecture 8 Tokenization , bigrams, trigrams, and N gram - Hands on

    Lecture 9 POS Tagging & Stop Words Removal

    Lecture 10 Stemming & Lemmatization

    Lecture 11 NER and Wordsense Disambiguation

    Lecture 12 Introduction to Spacy Library

    Lecture 13 Hands On Spacy

    Lecture 14 Summary

    Section 3: NLP -Text Vectorization

    Lecture 15 Vector Representation of Text - One Hot Encoding

    Lecture 16 Understanding BoW Technique

    Lecture 17 BoW Hands On

    Lecture 18 TF-IDF

    Lecture 19 TF-IDF Hands On

    Section 4: Word Embeddings

    Lecture 20 Introduction to Word Embeddings

    Lecture 21 Intuition of Vector Representation

    Lecture 22 Hands On Word Embeddings - Usage of Pre-trained models

    Lecture 23 Skip-gram Word Embeddings - Understanding Data Preperation

    Lecture 24 Skip Gram Model Architecture

    Lecture 25 Skip Gram Hands On - Deep Dive

    Lecture 26 CBOW Model Architecture & Hands On

    Lecture 27 Hyperparameters - Negative Sampling and Sub Sampling

    Lecture 28 Practical Difference between CBOW and Skip-gram

    Lecture 29 Bonus : How does a Network is trained - Back-propagation

    Lecture 30 Section Summary

    Section 5: End to End Pipeline for Text Classification

    Lecture 31 General Pipeline for Classification

    Lecture 32 Approaches to Classification

    Lecture 33 Loading the Dataset

    Lecture 34 Exploratory Data Analysis & Text Preprocessing

    Lecture 35 Remove Low Frequency Words

    Lecture 36 Remove Stop Words with Stemming & Lemmatisation

    Lecture 37 Application of Model

    Lecture 38 TfIDF Approach

    Lecture 39 Challenges of NLP & N-grams

    Section 6: Information Extraction

    Lecture 40 Introduction to NER

    Lecture 41 Understanding CRF - Introduction

    Section 7: Chatbots - Build with Google Cloud Service - Dialogflow

    Lecture 42 Understanding Chatbots

    Lecture 43 Building a Simple Chatbot

    Lecture 44 Hands On Building a Simple FAQ Chatbot

    Lecture 45 Types of Chatbot and Pipeline for Chatbot

    Lecture 46 Terminologies in Chatbot

    Lecture 47 Dialog flow - Introduction

    Lecture 48 Basics of Dialogflow

    Lecture 49 Dialogflow system setup

    Lecture 50 Create Dialogflow chatbot

    Lecture 51 Dialogflow Fulfilment

    Lecture 52 Dialogflow Integrations/Deployment

    Lecture 53 Dialogflow Miscellaneous Tools

    Section 8: Deep Dive into the Dialog Systems (Chatbot)

    Lecture 54 Deep Dive into the components of Dialog System

    Lecture 55 Dialog Intent Prediction

    Lecture 56 Deep Learning based intent Classification

    Section 9: Project - Build Chatbot using RASA

    Lecture 57 Project Files for RASA

    Lecture 58 Introduction to RASA Chatbot

    Lecture 59 Installation of RASA

    Lecture 60 RASA project Structure

    Lecture 61 RASA Files

    Lecture 62 Basics of YAML

    Lecture 63 Building the chatbot - Add intents and Response

    Lecture 64 Building the chatbot - Extract Entity & working with Slots

    Lecture 65 Create API Key from NyTimes

    Lecture 66 Working with Action File - Demo

    Lecture 67 Building Custom Action File

    Lecture 68 Test the Action Server

    Lecture 69 RASA Pipeline file

    Lecture 70 RASA Deployment - Integration with RASA Chatbot - Pre-requisites

    Lecture 71 Run Ngork on RASA Chatbot with Actions

    Lecture 72 Slack Settings for Connection to RASA Chatbot

    Lecture 73 Practice Project Concert Chatbot & Summary

    Section 10: Text Summarization

    Lecture 74 Text Summarization - Introduction

    Lecture 75 Hands On Text Summarization

    Section 11: NLP Project - Analyze Tweets from Twitter

    Lecture 76 Importance of Social Media Platforms

    Lecture 77 Setting Up Twitter Developer Account

    Lecture 78 Introduction to Tweepy

    Lecture 79 Hands On Implementation of Project

    Section 12: Section 11 : Introduction to Transformers

    Lecture 80 NLP Transformers - Introduction

    Lecture 81 Feed Forward Neural Network and Challenges

    Lecture 82 RNN - Recurrent Neural Networks

    Lecture 83 LSTM - Long Short Term Memory Networks

    Lecture 84 Attention Mechanism - Attention is all you Need

    Lecture 85 Transfer Learning

    Lecture 86 Transformer Architecture Overview

    Lecture 87 Additional Video on Transformers

    Section 13: Working with Hugging Face Library

    Lecture 88 Introduction to Hugging Face Library

    Lecture 89 Working with Hugging Face Library Pipeline

    Lecture 90 Text Classification with HuggingFace Transformers - Data Loading

    Lecture 91 Tokenization using Huggingface

    Lecture 92 Tokenization on Dataset

    Lecture 93 Text Classification with Feature Extraction

    Lecture 94 Finetuning on Transformers

    Section 14: ChatGPT-3

    Lecture 95 Working with ChatGPT-3

    Section 15: Section 14: Advanced NLP Models - BERT

    Lecture 96 Working of BERT Language Model

    Anyone who wants to learn natural language processing (NLP),Anyone interested in artificial intelligence, machine learning, deep learning, or data science,Anyone who wants to build Advanced NLP models and implement in a project,Anyone who wants to create Enterprise Grade Chatbots