Certification in Natural Language Processing (NLP)

The Certification in Natural Language Processing (NLP) course provides a complete introduction to the field of NLP. It begins with foundational topics such as text processing, lemmatization, tokenization, and vectorization. Learners explore text cleaning techniques and the use of Python and Scikit-Learn for NLP tasks. The course then dives into text classification using supervised learning and deep learning, including CNNs and transformer-based models. Students gain insights into word and document embeddings and their applications. Key areas like named entity recognition, sentiment analysis, syntax and parsing, and machine translation are covered.

Course Curriculum

• Module 1: Introduction and study plan
• Module 2: Introduction to Natural Language Processing
• Module 3: Text Processing
• Module 4: Discourse and Pragmatics
• Module 5: Application of NLP
• Module 6: NLP is a rapidly evolving field
• Module 7: Basics of Text Processing with python
• Module 8: Python code
• Module 9: Text Cleaning
• Module 10: Python code
• Module 11: Lemmatization
• Module 12: TF-IDF Vectorization
• Module 13: Text Representation and Feature Engineering
• Module 14: Tokenization
• Module 15: Vectorization Process
• Module 16: Bag of Words Representation
• Module 17: Example Code using scikit-Learn
• Module 18: Word Embeddings
• Module 19: Distributed Representation
• Module 20: Properties of Word Embeddings
• Module 21: Using Work Embeddings
• Module 22: Document Embeddings
• Module 23: purpose of Document Embeddings
• Module 24: Training Document Embeddings
• Module 25: Using Document Embeddings
• Module 26: Continuation of Using Document Embeddings
• Module 27: Supervised Learning for Text Classification
• Module 28: Model Selection
• Module 29: Model Training
• Module 30: Model Deployment
• Module 31: Continuation of Model Deployment
• Module 32: Deep Learning for Text Classification
• Module 33: Convolutional Neural Networks
• Module 34: Transformer Based Model
• Module 35: Model Evaluation and fine tuning
• Module 36: Continuation of Model Evaluation and fine tuning
• Module 37: Named Entity Recognition and Parts of Speech Tagging
• Module 38: Named Entity Recognition
• Module 39: Part of Speech Tagging
• Module 40: Relationship Between NER and POS Tagging
• Module 41: Syntax and parsing in NLP
• Module 42: Syntax
• Module 43: Grammar
• Module 44: Application in NLP
• Module 45: Challenges
• Module 46: Dependency Parsing
• Module 47: Dependency Relations
• Module 48: Dependency Parse Trees
• Module 49: Applications of Dependency Parsing
• Module 50: Challenges
• Module 51: Basics of Sentiment Analysis and Opinion Mining
• Module 52: Understanding Sentiment
• Module 53: Sentiment Analysis Techniques
• Module 54: Sentiment Analysis Application
• Module 55: Challenges and Limitations
• Module 56: Aspect-Based Sentiment Analysis
• Module 57: Key Components
• Module 58: Techniques and Approaches
• Module 59: Application
• Module 60: Continuation of Application
• Module 61: Machine Translation
• Module 62: Types of Machine Translation
• Module 63: Training NMT Models
• Module 64: Challenges in Machine Translation
• Module 65: Application of Machine Translation
• Module 66: Language Generation
• Module 67: Types of Language Generation
• Module 68: Applications of Language Generation
• Module 69: Challenges in Language Generation
• Module 70: Future Directions
• Module 71: Text Summarization and Question Answering
• Module 72: Text Summarization
• Module 73: Question Answering
• Module 74: Techniques and Approaches
• Module 75: Application
• Module 76: Challenges
• Module 77: Advanced Topics in NLP
• Module 78: Recurrent Neural Networks
• Module 79: Transformer
• Module 80: Generative pre trained Transformer(GPT)
• Module 81: Transfer LEARNING AND FINE TUNING
• Module 82: Ethical and Responsible AI in NLP
• Module 83: Transparency and Explainability
• Module 84: Ethical use Cases and Application
• Module 85: Continuous Monitoring and Evaluation
• Module 86: NLP Application and Future Trends
• Module 87: Customer service and Support Chatbots
• Module 88: Content Categorization and Recommendation
• Module 89: Voice Assistants and Virtual Agents
• Module 90: Healthcare and Medical NLP
• Module 91: Future Trends in NLP
• Module 92: Multimodal NLP
• Module 93: Ethical and Responsible AI
• Module 94: Domain Specific NLP
• Module 95: Continual Learning and Lifelong Adaptation
• Module 96: Capstone Project
• Module 97: Project Components
• Module 98: Model Selection and Training
• Module 99: Deployment and Application
• Module 100: Assessment Criteria
• Module 101: Additional Resources and Practice
• Module 102: Assignment

Key Features :

• Accredited by CPD
• Top-notch video lessons
• Instant e-certificate
• Entirely online, interactive course with audio voiceover
• Self-paced learning and laptop, tablet, and smartphone-friendly
• 24/7 Learning Assistance
• Discounts on bulk purchases

Digital certificate

Digital certificate - Included

Hard copy certificate

Hard copy certificate - Included

Reed Courses Certificate of Completion

Digital certificate - Included

• Certification in Natural Language Processing (NLP) 10:57:52

  • 1: 1. Introduction and study plan 03:22
  • 2: 2 Module 1.1.1 Introduction to Natural Language Processing 04:53
  • 3: 3 Module 1.1.2 Text Processing 07:29
  • 4: 4 Module 1.1.3 Discourse and Pragmatics 04:51
  • 5: 5 Module 1.1.4 Application of NLP 05:55
  • 6: 6 Module 1.1.5 NLP is a rapidly evolving field 02:52
  • 7: 7 Module 1.2.1 Basics of Text Processing with python 06:27
  • 8: 8 Module 1.2.2 Python code 06:03
  • 9: 9 Module 1.2.3 Text Cleaning 06:14
  • 10: 10 Module 1.2.4 Python code 07:27
  • 11: 11 Module 1.2.5 Lemmatization 11:40
  • 12: 12 Module 1.2.6 TF-IDF Vectorization 03:59
  • 13: 13 Module 2.1.1 Text Representation and Feature Engineering 03:28
  • 14: 14 Module 2.1.2 Tokenization 03:15
  • 15: 15 Module 2.1.3 Vectorization Process 02:41
  • 16: 16 Module 2.1.4 Bag of Words Representation 03:14
  • 17: 17 Module 2.1.5 Example Code using scikit-Learn 05:12
  • 18: 18 Module 2.2.1 Word Embeddings 03:36
  • 19: 19 Module 2.2.2 Distributed Representation 07:28
  • 20: 20 Module 2.2.3 Properties of Word Embeddings 09:34
  • 21: 21 Module 2.2.4 Using Work Embeddings 13:26
  • 22: 22 Module 2.3.1 Document Embeddings 04:13
  • 23: 23 Module 2.3.2 purpose of Document Embeddings 06:37
  • 24: 24 Module 2.3.3 Training Document Embeddings 04:10
  • 25: 25 Module 2.3.4 Using Document Embeddings 12:29
  • 26: 26 Module 2.3.4 Continuation of Using Document Embeddings 03:57
  • 27: 27 Module 3.1.1 Supervised Learning for Text Classification 08:16
  • 28: 28 Module 3.1.2 Model Selection 09:34
  • 29: 29 Module 3.1.3 Model Training 06:22
  • 30: 30 Module 3.1.4 Model Deployment 07:36
  • 31: 31 Module 3.1.4 Continuation of Model Deployment 03:02
  • 32: 32 Module 3.2.1 Deep Learning for Text Classification 08:11
  • 33: 33 Module 3.2.2 Convolutional Neural Networks 09:00
  • 34: 34 Module 3.2.3 Transformer Based Model 09:41
  • 35: 35 Module 3.2.4 Model Evaluation and fine tuning 06:43
  • 36: 36 Module 3.2.4 Continuation of Model Evaluation and fine tuning 04:24
  • 37: 37 Module 4.1.1 Named Entity Recognition and Parts of Speech Tagging 05:03
  • 38: 38 Module 4.1.2 Named Entity Recognition 04:06
  • 39: 39 Module 4.1.3 Part of Speech Tagging 04:52
  • 40: 40 Module 4.1.4 Relationship Between NER and POS Tagging 05:59
  • 41: 41 Module 5.1.1 Syntax and parsing in NLP 05:01
  • 42: 42 Module 5.1.2 Syntax 05:36
  • 43: 43 Module 5.1.3 Grammar 05:12
  • 44: 44 Module 5.1.4 Application in NLP 04:59
  • 45: 45 Module 5.1.5 Challenges 06:20
  • 46: 46 Module 5.2.1 Dependency Parsing 06:06
  • 47: 47 Module 5.2.2 Dependency Relations 03:35
  • 48: 48 Module 5.2.3 Dependency Parse Trees 06:12
  • 49: 49 Module 5.2.4 Applications of Dependency Parsing 04:36
  • 50: 50 Module 5.2.5 Challenges 10:03
  • 51: 51 Module 6.1.1 Basics of Sentiment Analysis and Opinion Mining 06:29
  • 52: 52 Module 6.1.2 Understanding Sentiment 12:09
  • 53: 53 Module 6.1.3 Sentiment Analysis Techniques 05:18
  • 54: 54 Module 6.1.4 Sentiment Analysis Application 05:17
  • 55: 55 Module 6.1.5 Challenges and Limitations 06:39
  • 56: 56 Module 6.2.1 Aspect-Based Sentiment Analysis 04:39
  • 57: 57 Module 6.2.2 Key Components 09:28
  • 58: 58 Module 6.2.3 Techniques and Approaches 03:13
  • 59: 59 Module 6.2.4 Application 07:36
  • 60: 60 Module 6.2.4 Continuation of Application 03:33
  • 61: 61 Module 7.1.1 Machine Translation 04:40
  • 62: 62 Module 7.1.2 Types of Machine Translation 08:09
  • 63: 63 Module 7.1.3 Training NMT Models 08:57
  • 64: 64 Module 7.1.4 Challenges in Machine Translation 04:04
  • 65: 65 Module 7.1.5 Application of Machine Translation 04:31
  • 66: 66 Module 7.2.1 Language Generation 04:38
  • 67: 67 Module 7.2.2 Types of Language Generation 09:02
  • 68: 68 Module 7.2.3 Applications of Language Generation 05:21
  • 69: 69 Module 7.2.4 Challenges in Language Generation 09:10
  • 70: 70 Module 7.2.5 Future Directions 06:49
  • 71: 71 Module 8.1.1 Text Summarization and Question Answering 06:33
  • 72: 72 Module 8.1.2 Text Summarization 10:44
  • 73: 73 Module 8.1.3 Question Answering 06:06
  • 74: 74 Module 8.1.4 Techniques and Approaches 06:07
  • 75: 75 Module 8.1.5 Application 05:21
  • 76: 76 Module 8.1.6 Challenges 06:41
  • 77: 77 Module 9.1.1 Advanced Topics in NLP 03:37
  • 78: 78 Module 9.1.2 Recurrent Neural Networks 04:18
  • 79: 79 Module 9.1.3 Transformer 05:51
  • 80: 80 Module 9.1.4 Generative pre trained Transformer(GPT) 05:59
  • 81: 81 Module 9.1.5 Transfer LEARNING AND FINE TUNING 04:12
  • 82: 82 Module 9.2.1 Ethical and Responsible AI in NLP 08:14
  • 83: 83 Module 9.2.2 Transparency and Explainability 08:54
  • 84: 84 Module 9.2.3 Ethical use Cases and Application 10:21
  • 85: 85 Module 9.2.4 Continuous Monitoring and Evaluation 05:30
  • 86: 86 Module 10.1.1 NLP Application and Future Trends 04:18
  • 87: 87 Module 10.1.2 Customer service and Support Chatbots 07:53
  • 88: 88 Module 10.1.3 Content Categorization and Recommendation 07:31
  • 89: 89 Module 10.1.4 Voice Assistants and Virtual Agents 04:57
  • 90: 90 Module 10.1.5 Healthcare and Medical NLP 09:07
  • 91: 91 Module 10.2.1 Future Trends in NLP 06:49
  • 92: 92 Module 10.2.2 Multimodal NLP 06:53
  • 93: 93 Module 10.2.3 Ethical and Responsible AI 08:47
  • 94: 94 Module 10.2.4 Domain Specific NLP 08:32
  • 95: 95 Module 10.2.5 Continual Learning and Lifelong Adaptation 02:31
  • 96: 96 Module 11.1.1 Capstone Project 05:26
  • 97: 97 Module 11.1.2 Project Components 10:09
  • 98: 98 Module 11.1.3 Model Selection and Training 09:33
  • 99: 99 Module 11.1.4 Deployment and Application 16:10
  • 100: 100 Module 11.1.5 Assessment Criteria 10:39
  • 101: 101 Module 11.1.6 Additional Resources and Practice 04:22
  • 102: 102 Assignment 01:04

Learners also explore natural language generation, summarization, and question answering. Advanced modules touch on GPT, transfer learning, and ethical AI. The course concludes with a capstone project where learners practice model training and deployment. It also explores current trends such as chatbots, recommendation systems, voice assistants, and domain-specific NLP. A final module offers assignments and extra resources to support continued learning.

The course also explores the structure and meaning of language through syntax, grammar, and parsing techniques. Learners will study dependency parsing, its applications, and the challenges it presents. The curriculum includes focused modules on sentiment analysis, including aspect-based methods and opinion mining. Machine translation and language generation are discussed in-depth, along with real-world use cases and associated difficulties. The program highlights the latest advancements in the field, such as recurrent neural networks, transformers, and GPT models. It emphasizes the importance of ethical AI, transparency, and the responsible use of language technologies. The final modules guide learners through building and deploying NLP models with a capstone project, helping to consolidate their understanding of key topics and trends in modern NLP.

Learning Outcomes

• Understand basics of NLP and text processing using Python tools.
• Learn feature engineering with tokenization, TF-IDF, and embeddings.
• Perform classification with deep learning and transformer models.
• Apply NLP to sentiment analysis and machine translation challenges.
• Explore text summarization, question answering, and content generation.
• Identify ethical concerns and responsible AI use in NLP systems.

• People new to Natural Language Processing (NLP).
• Students learning text analysis and classification techniques.
• Anyone interested in chatbot and voice assistant design.
• Beginners wanting to use Python for NLP tasks.
• Learners exploring modern NLP applications and tools.

• NLP Research Assistant
• AI Chatbot Developer
• Text Classification Analyst
• Sentiment Analysis Specialist
• Machine Translation Technician
• Virtual Assistant Designer

Currently there are no Q&As for this course. Be the first to ask a question.