Certification In Natural Language Processing (Nlp)

Learn Natural Language Processing Concepts, complete process, application and coding for any data science enthusiast

What you'll learn

You will learn the key concepts in Natural Language Processing (NLP), starting with an introduction to NLP and its foundational principles.

The course covers text representation and feature engineering, which are crucial for understanding and manipulating textual data

You will delve into text classification methods, which are essential for categorizing and organizing text.

The course includes named entity recognition (NER) and part-of-speech (POS) tagging, both of which are vital for extracting meaningful information from text.

You will be able to learn about syntax and parsing, including their roles in understanding and analyzing the structure of sentences.

Details about sentiment analysis and opinion mining, as well as machine translation and language generation

Learn about machine translation and language generation, including techniques for translating text between languages and generating coherent and contextually

text summarization and question answering, focusing on methods for condensing long texts into concise summaries and building systems that can answer questions

You will explore advanced topics in NLP, which delve into cutting-edge research and applications in the field.

Learn about NLP applications and future trends, focusing on how natural language processing is utilized in various industries and exploring the latest advance

You will also learn about the role of NLP in various applications and its integration with different technologies.

Requirements

You should have an interest in Natural Language Processing (NLP) and its applications.

An interest in text representation and feature engineering. Text classification. Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging. Syntax and Parsing. Sentiment Analysis and Opinion Mining. Machine Translation and Language Generation. Text Summarization and Question Answering. Advanced Topics in NLP. NLP Applications and Future Trends. Capstone Project.

Be interested in getting the knowledge of sentiment analysis and opinion mining, machine translation and language generation, text summarization and question answering.

Have an interest in understanding NLP applications and future trends, advanced topics in NLP, and the capstone project.

Description

DescriptionTake the next step in your career as data science professionals! Whether you’re an up-and-coming data scientist, an experienced data analyst, aspiring machine learning engineer, or budding AI researcher, this course is an opportunity to sharpen your data management and analytical capabilities, increase your efficiency for professional growth, and make a positive and lasting impact in the field of data science and analytics.With this course as your guide, you learn how to:● All the fundamental functions and skills required for Natural Language Processing (NLP).● Transform knowledge of NLP applications and techniques, text representation and feature engineering, sentiment analysis and opinion mining.● Get access to recommended templates and formats for details related to NLP applications and techniques.● Learn from informative case studies, gaining insights into NLP applications and techniques for various scenarios. Understand how the International Monetary Fund, monetary policy, and fiscal policy impact NLP advancements, with practical forms and frameworks.● Invest in expanding your NLP knowledge today and reap the benefits for years to come.The Frameworks of the CourseEngaging video lectures, case studies, assessments, downloadable resources, and interactive exercises. This course is designed to explore the NLP field, covering various chapters and units. You'll delve into text representation, feature engineering, text classification, NER, POS tagging, syntax, parsing, sentiment analysis, opinion mining, machine translation, language generation, text summarization, question answering, advanced NLP topics, and future trends.The socio-cultural environment module using NLP techniques delves into India's sentiment analysis and opinion mining, text summarization and question answering, and machine translation and language generation. It also applies NLP to explore the syntax and parsing, named entity recognition (NER), part-of-speech (POS) tagging, and advanced topics in NLP. You'll gain insight into NLP-driven analysis of sentiment analysis and opinion mining, text summarization and question answering, and machine translation and language generation. Furthermore, the content discusses NLP-based insights into NLP applications and future trends, along with a capstone project in NLP.The course includes multiple global NLP projects, resources like formats, templates, worksheets, reading materials, quizzes, self-assessment, film study, and assignments to nurture and upgrade your global NLP knowledge in detail.In the first part of the course, you’ll learn the details of the Indian business environment, Industrial policy and regulatory structures, its relation to the Economic and political environment of the business. Monetary policy and fiscal Policy.In the middle part of the course, you’ll learn how to develop a knowledge Socio cultural Environment, Legal environment. Foreign exchange management. Foreign trade and EXIM Policy.In the final part of the course, you’ll develop the knowledge related to the International Monetary funds, World Trade Organization and changes in business environment. You will get full support and all your quarries would be answered guaranteed within 48 hours.Course Content:Part 1Introduction and Study Plan● Introduction and know your Instructor● Study Plan and Structure of the Course1. Introduction to Natural Language Processing1.1.1 Introduction to Natural Language Processing1.1.2 Text Processing1.1.3 Discourse and Pragmatics1.1.4 Application of NLP1.1.5 NLP is a rapidly evolving field1.2.1 Basics of Text Processing with python1.2.2 Python code1.2.3 Text Cleaning1.2.4 Python code1.2.5 Lemmatization1.2.6 TF-IDF Vectorization2. Text Representation and Feature Engineering2.1.1 Text Representation and Feature Engineering2.1.2 Tokenization2.1.3 Vectorization Process2.1.4 Bag of Words Representation2.1.5 Example Code using scikit-Learn2.2.1 Word Embeddings2.2.2 Distributed Representation2.2.3 Properties of Word Embeddings2.2.4 Using Work Embeddings2.3.1 Document Embeddings2.3.2 purpose of Document Embeddings2.3.3 Training Document Embeddings2.3.4 Using Document Embeddings3. Text Classification3.1.1 Supervised Learning for Text Classification3.1.2 Model Selection3.1.3 Model Training3.1.4 Model Deployment3.2.1 Deep Learning for Text Classification3.2.2 Convolutional Neural Networks3.2.3 Transformer Based Model3.2.4 Model Evaluation and fine tuning4. Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging4.1.1 Named Entity Recognition and Parts of Speech Tagging4.1.2 Named Entity Recognition4.1.3 Part of Speech Tagging4.1.4 Relationship Between NER and POS Tagging5. Syntax and Parsing5.1.1 Syntax and parsing in NLP5.1.2 Syntax5.1.3 Grammar5.1.4 Application in NLP5.1.5 Challenges5.2.1 Dependency Parsing5.2.2 Dependency Relations5.2.3 Dependency Parse Trees5.2.4 Applications of Dependency Parsing5.2.5 Challenges6. Sentiment Analysis and Opinion Mining6.1.1 Basics of Sentiment Analysis and Opinion Mining6.1.2 Understanding Sentiment6.1.3 Sentiment Analysis Techniques6.1.4 Sentiment Analysis Application6.1.5 Challenges and Limitations6.2.1 Aspect-Based Sentiment Analysis6.2.2 Key Components6.2.3 Techniques and Approaches6.2.4 Application6.2.4 Continuation of Application7. Machine Translation and Language Generation7.1.1 Machine Translation7.1.2 Types of Machine Translation7.1.3 Training NMT Models7.1.4 Challenges in Machine Translation7.1.5 Application of Machine Translation7.2.1 Language Generation7.2.2 Types of Language Generation7.2.3 Applications of Language Generation7.2.4 Challenges in Language Generation7.2.5 Future Directions8. Text Summarization and Question Answering8.1.1 Text Summarization and Question Answering8.1.2 Text Summarization8.1.3 Question Answering8.1.4 Techniques and Approaches8.1.5 Application8.1.6 Challenges9. Advanced Topics in NLP9.1.1 Advanced Topics in NLP9.1.2 Recurrent Neural Networks9.1.3 Transformer9.1.4 Generative pre trained Transformer(GPT)9.1.5 Transfer LEARNING AND FINE TUNING9.2.1 Ethical and Responsible AI in NLP9.2.2 Transparency and Explainability9.2.3 Ethical use Cases and Application9.2.4 Continuous Monitoring and Evaluation10. NLP Applications and Future Trends10.1.1 NLP Application and Future Trends10.1.2 Customer service and Support Chatbots10.1.3 Content Categorization and Recommendation10.1.4 Voice Assistants and Virtual Agents10.1.5 Healthcare and Medical NLP10.2.1 Future Trends in NLP10.2.2 Multimodal NLP10.2.3 Ethical and Responsible AI10.2.4 Domain Specific NLP10.2.5 Continual Learning and Lifelong Adaptation11. Capstone Project11.1.1 Capstone Project11.1.2 Project Components11.1.3 Model Selection and Training11.1.4 Deployment and Application11.1.5 Assessment Criteria11.1.6 Additional Resources and PracticePart 3Assignments

Overview

Section 1: 1. Introduction to Natural Language Processing

Lecture 1 Introduction and Study Plan

Lecture 2 Introduction to Natural Language Processing

Lecture 3 Text Processing

Lecture 4 Discourse and Pragmatics

Lecture 5 Application of NLP

Lecture 6 NLP is a rapidly evolving field

Lecture 7 Basics of Text Processing with python

Lecture 8 Python code

Lecture 9 Text Cleaning

Lecture 10 Python code2

Lecture 11 Lemmatization

Lecture 12 TF-IDF Vectorization

Section 2: 2. Text Representation and Feature Engineering

Lecture 13 Text Representation and Feature Engineering

Lecture 14 Tokenization

Lecture 15 Vectorization Process

Lecture 16 Bag of Words Representation

Lecture 17 Example Code using scikit-Learn

Lecture 18 Word Embeddings

Lecture 19 Distributed Representation

Lecture 20 Properties of Word Embeddings

Lecture 21 Using Work Embeddings

Lecture 22 Document Embeddings

Lecture 23 Purpose of Document Embeddings

Lecture 24 Training Document Embeddings

Lecture 25 Using Document Embeddings

Lecture 26 Using Document Embeddings2

Section 3: 3. Text Classification

Lecture 27 Supervised Learning for Text Classification

Lecture 28 Model Selection

Lecture 29 Model Training

Lecture 30 Model Deployment

Lecture 31 Model Deployment2

Lecture 32 Deep Learning for Text Classification

Lecture 33 Convolutional Neural Networks

Lecture 34 Transformer Based Model

Lecture 35 Model Evaluation and fine tuning

Lecture 36 Model Evaluation and fine tuning2

Section 4: 4. Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging

Lecture 37 Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging

Lecture 38 Named Entity Recognition2

Lecture 39 Part of Speech Tagging

Lecture 40 Relationship Between NER and POS Tagging

Section 5: 5. Syntax and Parsing

Lecture 41 Syntax and Parsing

Lecture 42 Syntax

Lecture 43 Grammar

Lecture 44 Application in NLP

Lecture 45 Challenges

Lecture 46 Dependency Parsing

Lecture 47 Dependency Relations

Lecture 48 Dependency Parse Trees

Lecture 49 Applications of Dependency Parsing

Lecture 50 Challenges

Section 6: 6. Sentiment Analysis and Opinion Mining

Lecture 51 Basics of Sentiment Analysis and Opinion Mining

Lecture 52 Understanding Sentiment

Lecture 53 Sentiment Analysis Techniques

Lecture 54 Sentiment Analysis Application

Lecture 55 Challenges and Limitations

Lecture 56 Aspect-Based Sentiment Analysis

Lecture 57 Key Components

Lecture 58 Techniques and Approaches

Lecture 59 Application

Lecture 60 Continuation of Application

Section 7: 7. Machine Translation and Language Generation

Lecture 61 Machine Translation

Lecture 62 Types of Machine Translation

Lecture 63 Training NMT Models

Lecture 64 Challenges in Machine Translation

Lecture 65 Application of Machine Translation

Lecture 66 Language Generation

Lecture 67 Types of Language Generation

Lecture 68 Applications of Language Generation

Lecture 69 Challenges in Language Generation

Lecture 70 Future Directions

Section 8: 8. Text Summarization and Question Answering

Lecture 71 Text Summarization and Question Answering

Lecture 72 Text Summarization

Lecture 73 Question Answering

Lecture 74 Techniques and Approaches

Lecture 75 Application

Lecture 76 Challenges

Section 9: 9. Advanced Topics in NLP

Lecture 77 Advanced Topics in NLP

Lecture 78 Recurrent Neural Networks

Lecture 79 Transformer

Lecture 80 Generative pre trained Transformer(GPT)

Lecture 81 Transfer Learning and Fine tuning

Lecture 82 Ethical and Responsible AI in NLP

Lecture 83 Transparency and Explainability

Lecture 84 Ethical use Cases and Application

Lecture 85 Continuous Monitoring and Evaluation

Section 10: 10. NLP Applications and Future Trends

Lecture 86 NLP Applications and Future Trends

Lecture 87 Customer service and Support Chatbots

Lecture 88 Content Categorization and Recommendation

Lecture 89 Voice Assistants and Virtual Agents

Lecture 90 Healthcare and Medical NLP

Lecture 91 Future Trends in NLP

Lecture 92 Multimodal NLP

Lecture 93 Ethical and Responsible AI

Lecture 94 Domain Specific NLP

Lecture 95 Continual Learning and Lifelong Adaptation

Section 11: 11. Capstone Project

Lecture 96 Capstone Project

Lecture 97 Project Components

Lecture 98 Model Selection and Training

Lecture 99 Deployment and Application

Lecture 100 Assessment Criteria

Lecture 101 Additional Resources and Practice

Section 12: Assignments and Project

Lecture 102 Assignment

Lecture 103 Project : Sentiment Analysis of Customer Reviews

Lecture 104 Coding samples for Natural language processing

Professionals with a deep understanding of NLP applications, advanced topics in NLP, and a desire to excel in the field of natural language processing.,New professionals aiming for success in NLP applications and the economic environment of business.,Existing executive board directors, managing directors who are seeking greater engagement and innovation from their teams and organizations.