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Certification in Natural Language Processing (NLP)

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2 STUDENTS

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Natural Language Processing (NLP) sits at the heart of modern AI—where linguistics meets data science to give machines the ability to interpret, analyse, and generate human language. This course offers a thorough exploration of NLP concepts, tools, and techniques, presented through structured modules designed to build both foundational knowledge and advanced understanding. From text processing and sentiment analysis to transformers and ethical AI, you’ll be navigating a full spectrum of NLP’s ever-expanding capabilities.

Whether you’re looking to decode the mechanics of machine translation or explore language generation using neural networks, this course walks you through a wide range of applications—from customer service automation to healthcare and virtual agents. It’s all delivered in an accessible, structured, and human-readable format (not unlike the dream syntax of an AI with manners). 

This Certification in Natural Language Processing (NLP) offers a rich, engaging way to explore one of the most fascinating domains in AI—whether you’re decoding the linguistic layers of a tweet or designing a transformer model with impeccable syntax. You’ll leave not just fluent in NLP terminology, but also equipped to apply your skills across industries driven by automation, conversation, and machine understanding.

  • Accredited by CPD
  • Instant e-certificate and hard copy dispatch by next working day
  • Fully online, interactive course with audio voiceover
  • Self-paced learning and laptop, tablet, smartphone-friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Sneak Peek

Document Embeddings Using Document Embeddings Model Training Model Evaluation and fine tuning

Learning Outcomes

  • Understand NLP fundamentals and modern language processing techniques
  • Explore text representation methods using Python and machine learning
  • Apply classification models to various NLP-based tasks and challenges
  • Evaluate models for accuracy, relevance, and ethical considerations
  • Discover applications in sentiment analysis, translation, and summarisation
  • Implement neural networks and transformer models for NLP projects

Who is this Course For

  • Aspiring data scientists wanting to specialise in NLP solutions
  • Python users curious about text analytics and machine learning
  • Tech professionals interested in language-based AI systems
  • Developers exploring tools like GPT and word embeddings
  • Academic researchers studying linguistic computing and AI
  • Analysts aiming to work with customer insights and chatbots
  • Engineers developing smart assistants or recommendation engines
  • Career changers exploring NLP with a flexible online structure

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Accreditation

All of our courses, including this Certification in Natural Language Processing (NLP), are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.

Career Path

  • NLP Engineer – Average Salary: £65,000/year
  • Machine Learning Engineer – Average Salary: £70,000/year
  • AI Research Scientist – Average Salary: £75,000/year
  • Data Scientist (NLP Focus) – Average Salary: £60,000/year
  • Computational Linguist – Average Salary: £55,000/year
  • Chatbot Developer – Average Salary: £50,000/year

Course Curriculum

The detailed curriculum outline of our Certification in Natural Language Processing (NLP) is as follows:

  •  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 

Course Curriculum

Certification in Natural Language Processing (NLP)
Module 1: Introduction and study plan 00:03:00
Module 2: Introduction to Natural Language Processing 00:05:00
Module 3: Text Processing 00:07:00
Module 4: Discourse and Pragmatics 00:05:00
Module 5: Application of NLP 00:06:00
Module 6: NLP is a rapidly evolving field 00:03:00
Module 7: Basics of Text Processing with python 00:06:00
Module 8: Python code 00:06:00
Module 9: Text Cleaning 00:06:00
Module 10: Python code 00:07:00
Module 11: Lemmatization 00:12:00
Module 12: TF-IDF Vectorization 00:04:00
Module 13: Text Representation and Feature Engineering 00:03:00
Module 14: Tokenization 00:03:00
Module 15: Vectorization Process 00:03:00
Module 16: Bag of Words Representation 00:03:00
Module 17: Example Code using scikit-Learn 00:05:00
Module 18: Word Embeddings 00:04:00
Module 19: Distributed Representation 00:07:00
Module 20: Properties of Word Embeddings 00:10:00
Module 21: Using Work Embeddings 00:13:00
Module 22: Document Embeddings 00:04:00
Module 23: purpose of Document Embeddings 00:07:00
Module 24: Training Document Embeddings 00:04:00
Module 25: Using Document Embeddings 00:12:00
Module 26: Continuation of Using Document Embeddings 00:04:00
Module 27: Supervised Learning for Text Classification 00:08:00
Module 28: Model Selection 00:10:00
Module 29: Model Training 00:06:00
Module 30: Model Deployment 00:08:00
Module 31: Continuation of Model Deployment 00:03:00
Module 32: Deep Learning for Text Classification 00:08:00
Module 33: Convolutional Neural Networks 00:09:00
Module 34: Transformer Based Model 00:10:00
Module 35: Model Evaluation and fine tuning 00:07:00
Module 36: Continuation of Model Evaluation and fine tuning 00:04:00
Module 37: Named Entity Recognition and Parts of Speech Tagging 00:05:00
Module 38: Named Entity Recognition 00:04:00
Module 39: Part of Speech Tagging 00:05:00
Module 40: Relationship Between NER and POS Tagging 00:06:00
Module 41: Syntax and parsing in NLP 00:05:00
Module 42: Syntax 00:06:00
Module 43: Grammar 00:05:00
Module 44: Application in NLP 00:05:00
Module 45: Challenges 00:06:00
Module 46: Dependency Parsing 00:06:00
Module 47: Dependency Relations 00:04:00
Module 48: Dependency Parse Trees 00:06:00
Module 49: Applications of Dependency Parsing 00:05:00
Module 50: Challenges 00:10:00
Module 51: Basics of Sentiment Analysis and Opinion Mining 00:06:00
Module 52: Understanding Sentiment 00:12:00
Module 53: Sentiment Analysis Techniques 00:05:00
Module 54: Sentiment Analysis Application 00:05:00
Module 55: Challenges and Limitations 00:07:00
Module 56: Aspect-Based Sentiment Analysis 00:05:00
Module 57: Key Components 00:09:00
Module 58: Techniques and Approaches 00:03:00
Module 59: Application 00:08:00
Module 60: Continuation of Application 00:04:00
Module 61: Machine Translation 00:05:00
Module 62: Types of Machine Translation 00:08:00
Module 63: Training NMT Models 00:09:00
Module 64: Challenges in Machine Translation 00:04:00
Module 65: Application of Machine Translation 00:05:00
Module 66: Language Generation 00:05:00
Module 67: Types of Language Generation 00:09:00
Module 68: Applications of Language Generation 00:05:00
Module 69: Challenges in Language Generation 00:09:00
Module 70: Future Directions 00:07:00
Module 71: Text Summarization and Question Answering 00:07:00
Module 72: Text Summarization 00:11:00
Module 73: Question Answering 00:06:00
Module 74: Techniques and Approaches 00:06:00
Module 75: Application 00:05:00
Module 76: Challenges 00:07:00
Module 77: Advanced Topics in NLP 00:04:00
Module 78: Recurrent Neural Networks 00:04:00
Module 79: Transformer 00:06:00
Module 80: Generative pre trained Transformer(GPT) 00:06:00
Module 81: Transfer LEARNING AND FINE TUNING 00:04:00
Module 82: Ethical and Responsible AI in NLP 00:08:00
Module 83: Transparency and Explainability 00:09:00
Module 84: Ethical use Cases and Application 00:10:00
Module 85: Continuous Monitoring and Evaluation 00:06:00
Module 86: NLP Application and Future Trends 00:04:00
Module 87: Customer service and Support Chatbots 00:08:00
Module 88: Content Categorization and Recommendation 00:08:00
Module 89: Voice Assistants and Virtual Agents 00:05:00
Module 90: Healthcare and Medical NLP 00:09:00
Module 91: Future Trends in NLP 00:07:00
Module 92: Multimodal NLP 00:07:00
Module 93: Ethical and Responsible AI 00:09:00
Module 94: Domain Specific NLP 00:09:00
Module 95: Continual Learning and Lifelong Adaptation 00:03:00
Module 96: Capstone Project 00:05:00
Module 97: Project Components 00:10:00
Module 98: Model Selection and Training 00:10:00
Module 99: Deployment and Application 00:16:00
Module 100: Assessment Criteria 00:11:00
Module 101: Additional Resources and Practice 00:04:00
Module 102: Assignment 00:01:00
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