Certification in Natural Language Processing (NLP)

Description

Take 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 Course

Engaging 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 1

Introduction and Study Plan

● Introduction and know your Instructor

● Study Plan and Structure of the Course

1. Introduction to Natural Language Processing

1.1.1 Introduction to Natural Language Processing

1.1.2 Text Processing

1.1.3 Discourse and Pragmatics

1.1.4 Application of NLP

1.1.5 NLP is a rapidly evolving field

1.2.1 Basics of Text Processing with python

1.2.2 Python code

1.2.3 Text Cleaning

1.2.4 Python code

1.2.5 Lemmatization

1.2.6 TF-IDF Vectorization

2. Text Representation and Feature Engineering

2.1.1 Text Representation and Feature Engineering

2.1.2 Tokenization

2.1.3 Vectorization Process

2.1.4 Bag of Words Representation

2.1.5 Example Code using scikit-Learn

2.2.1 Word Embeddings

2.2.2 Distributed Representation

2.2.3 Properties of Word Embeddings

2.2.4 Using Work Embeddings

2.3.1 Document Embeddings

2.3.2 purpose of Document Embeddings

2.3.3 Training Document Embeddings

2.3.4 Using Document Embeddings

3. Text Classification

3.1.1 Supervised Learning for Text Classification

3.1.2 Model Selection

3.1.3 Model Training

3.1.4 Model Deployment

3.2.1 Deep Learning for Text Classification

3.2.2 Convolutional Neural Networks

3.2.3 Transformer Based Model

3.2.4 Model Evaluation and fine tuning

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

4.1.1 Named Entity Recognition and Parts of Speech Tagging

4.1.2 Named Entity Recognition

4.1.3 Part of Speech Tagging

4.1.4 Relationship Between NER and POS Tagging

5. Syntax and Parsing

5.1.1 Syntax and parsing in NLP

5.1.2 Syntax

5.1.3 Grammar

5.1.4 Application in NLP

5.1.5 Challenges

5.2.1 Dependency Parsing

5.2.2 Dependency Relations

5.2.3 Dependency Parse Trees

5.2.4 Applications of Dependency Parsing

5.2.5 Challenges

6. Sentiment Analysis and Opinion Mining

6.1.1 Basics of Sentiment Analysis and Opinion Mining

6.1.2 Understanding Sentiment

6.1.3 Sentiment Analysis Techniques

6.1.4 Sentiment Analysis Application

6.1.5 Challenges and Limitations

6.2.1 Aspect-Based Sentiment Analysis

6.2.2 Key Components

6.2.3 Techniques and Approaches

6.2.4 Application

6.2.4 Continuation of Application

7. Machine Translation and Language Generation

7.1.1 Machine Translation

7.1.2 Types of Machine Translation

7.1.3 Training NMT Models

7.1.4 Challenges in Machine Translation

7.1.5 Application of Machine Translation

7.2.1 Language Generation

7.2.2 Types of Language Generation

7.2.3 Applications of Language Generation

7.2.4 Challenges in Language Generation

7.2.5 Future Directions

8. Text Summarization and Question Answering

8.1.1 Text Summarization and Question Answering

8.1.2 Text Summarization

8.1.3 Question Answering

8.1.4 Techniques and Approaches

8.1.5 Application

8.1.6 Challenges

9. Advanced Topics in NLP

9.1.1 Advanced Topics in NLP

9.1.2 Recurrent Neural Networks

9.1.3 Transformer

9.1.4 Generative pre trained Transformer(GPT)

9.1.5 Transfer LEARNING AND FINE TUNING

9.2.1 Ethical and Responsible AI in NLP

9.2.2 Transparency and Explainability

9.2.3 Ethical use Cases and Application

9.2.4 Continuous Monitoring and Evaluation

10. NLP Applications and Future Trends

10.1.1 NLP Application and Future Trends

10.1.2 Customer service and Support Chatbots

10.1.3 Content Categorization and Recommendation

10.1.4 Voice Assistants and Virtual Agents

10.1.5 Healthcare and Medical NLP

10.2.1 Future Trends in NLP

10.2.2 Multimodal NLP

10.2.3 Ethical and Responsible AI

10.2.4 Domain Specific NLP

10.2.5 Continual Learning and Lifelong Adaptation

11. Capstone Project

11.1.1 Capstone Project

11.1.2 Project Components

11.1.3 Model Selection and Training

11.1.4 Deployment and Application

11.1.5 Assessment Criteria

11.1.6 Additional Resources and Practice

Part 3

Assignments