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Certified Natural Language Processing Expert Certification (CNLPE – AI3070)

  • Master NLP: Gain expertise in analyzing human language. Our Certified NLP Expert Certification equips you with skills to process textual data, unlocking powerful insights.
  • Stand Out in AI: Be a qualified professional in the growing field. Our recognized certification showcases NLP proficiency, positioning you as a sought-after expert.
  • Boost Career Opportunities: Open doors to exciting prospects. Employers seek NLP professionals in healthcare, finance, and technology. Elevate your resume with our comprehensive program for endless possibilities.
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Eligibility Test

Becoming a Certified Natural Language Processing Expert opens up a world of possibilities in the transformative technology. This certification showcases a deep understanding of NLP techniques, algorithms, and applications, giving individuals a significant edge in the competitive job market.

Employers actively seek out professionals with this expertise, making certified individuals highly desirable. With this certification, professionals are equipped to extract valuable insights from vast amounts of unstructured text data. Proficiency in text classification, sentiment analysis, named entity recognition, and language generation enables Natural Language Processing experts to develop cutting-edge solutions for a variety of industries, such as healthcare, finance, marketing, and customer service.

Certified Natural Language Processing Experts not only advance their careers but also contribute to the success of their organizations. NLP plays a crucial role in enhancing operational efficiency, boosting customer satisfaction, and staying ahead of the competition. Having this expertise enables individuals to unlock new opportunities and foster innovation.

Certification provides opportunities for individuals to pursue diverse careers in fields like data science, AI research, machine learning engineering, or consulting. The flexibility of Natural Language Processing allows professionals to delve into various domains and industries, constantly pushing their boundaries and broadening their perspectives.

To acquire this certification, aspiring individuals participate in a comprehensive training program that encompasses the fundamentals of NLP, machine learning, and deep learning. They gain practical experience with popular NLP libraries and frameworks, enabling them to develop solutions for real-world challenges. The certification process typically involves exams, projects, and evaluations to ensure a solid grasp of the subject matter.

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COURSE SYLLABUS

Introduction to Natural Language Processing

 

  •  Introduction to NLP and its applications
  •  Key concepts and challenges in NLP
  •  Overview of NLP libraries and frameworks

Text Preprocessing for NLP

 

  •  Tokenization and stemming
  •  Stop word removal and punctuation handling
  •  Text normalization techniques
  •  Part-of-speech tagging and syntactic parsing

Sentiment Analysis and Opinion Mining

 

  •  Introduction to sentiment analysis
  •  Feature extraction for sentiment analysis
  •  Sentiment classification algorithms (Naive Bayes, SVM, etc.)
  •  Aspect-based sentiment analysis
     

Named Entity Recognition (NER)

 

  •  Introduction to NER and entity types
  •  Rule-based and statistical NER approaches
  •  Named entity recognition with deep learning models
  •  NER evaluation and benchmark datasets
     

Language Modeling and Text Generation

 

  •  N-gram language models
  •  Neural language models (e.g., LSTM, Transformer)
  •  Text generation using language models
  •  Evaluation metrics for language models

 Machine Translation

 

  •  Introduction to machine translation
  •  Rule-based and statistical machine translation
  •  Neural machine translation (seq2seq models)
  •  Evaluation and metrics for machine translation

Question Answering Systems

 

  •  Overview of question answering systems
  •  Information retrieval for question answering
  •  Text comprehension and machine reading comprehension
  •  Evaluation of question answering systems
     

Text Summarization

 

  •  Extractive and abstractive summarization
  •  Text ranking algorithms for extractive summarization
  •  Sequence-to-sequence models for abstractive summarization
  •  Evaluation metrics for text summarization

Topic Modeling

 

  •  Latent Dirichlet Allocation (LDA)
  •  Probabilistic topic models
  •  Topic modeling evaluation
  •  Applications of topic modeling

Advanced NLP Techniques

 

  •  Neural network architectures for NLP (e.g., CNN, RNN)
  •  Attention mechanisms in NLP
  •  Transfer learning in NLP
  •  Ethical considerations in NLP
     

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Prerequisite Certification

  • Programming proficiency: A solid understanding of programming languages like Python is essential to work with NLP libraries and develop algorithms.
  • Statistical analysis and machine learning knowledge: Familiarity with concepts like classification, clustering, and regression is crucial to extract insights and create effective NLP models.
  • Linguistics and language processing understanding: A basic grasp of morphology, syntax, and semantics enables accurate analysis and interpretation of language patterns.
  • Experience with NLP tools and frameworks: Prior exposure to popular NLP tools like NLTK, spaCy, or TensorFlow is advantageous for streamlining development processes.
  • Problem-solving and critical thinking skills: NLP experts must be adept at creatively approaching language challenges, identifying patterns, and providing innovative solutions.

Who can pursue this certification?

Data Scientists and Machine Learning Engineers: Professionals with a background in data science and machine learning who want to specialize in NLP can pursue the certification to demonstrate their expertise in applying NLP algorithms, models, and techniques to analyze and process textual data.

NLP Researchers: Individuals engaged in research and development in the field of NLP, including areas such as sentiment analysis, text classification, language generation, or question answering systems, can pursue the certification to validate their advanced knowledge and contributions to the field.

NLP Engineers: Professionals working as NLP engineers or NLP software developers who design and build NLP applications, chatbots, or language processing systems can pursue the certification to showcase their expertise in developing and deploying NLP solutions.

AI Consultants and Advisors: Consultants specializing in NLP, AI strategy, or AI implementation can pursue the certification to enhance their credibility and demonstrate their expertise in guiding organizations through NLP adoption, developing NLP strategies, and implementing NLP solutions.

Researchers in Linguistics or Computational Linguistics: Individuals with a background in linguistics or computational linguistics who have an interest in applying their knowledge to practical NLP applications can pursue the certification to bridge the gap between theory and implementation.

Technology Leaders and Innovators: Individuals in leadership roles within technology companies or startups who want to establish themselves as experts in NLP can pursue the certification to strengthen their knowledge and credibility in the field.

Certified Natural Language Processing Expert

(Test Preparation Study Guide)

Get your free study guide for the Certified Natural Language Processing Expert Certification, the ultimate credential in NLP. Enhance your expertise and excel in the field of language processing. 

 

The Benefits

International Credential

IABAC is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

Global Opportunities

IABAC certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

Specialization

IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

Relevant and updated

IABAC CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

Higher Salaries

On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

Summits & Webinars

In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.

Not sure which certification suits your goal? Get a free counselling

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COURSE FAQs

How long does it take to get a Certified Natural Language Processing Expert Certification after the exam completion?

The time taken for Certified Natural Language Processing Expert certification after the exam completion is usually 10 working days.