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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.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

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