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Natural Language Processing (NLP) Certificate Online

Natural Language Processing (NLP) Certificate Online

Original price was: $300.00.Current price is: $149.00.

🌟🌟🌟🌟🌟 (19 reviews)

Number of Users Discount
2 - 10 30%
11 - 20 40%
21 - 50 50%
51 - 100 60%
101 + 70%
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Course Overview

The Natural Language Processing (NLP) Certificate Online is designed to equip students with the foundational and advanced knowledge required to excel in the field of NLP. This program covers a broad spectrum of topics from basic text processing to deep learning applications in NLP. The course is delivered entirely online, making it accessible to a global audience and allowing students to learn at their own pace.

Learning Outcomes

Upon successful completion of the Natural Language Processing Certificate Online, students will be able to:

  1. Understand and apply basic NLP techniques such as tokenization, stemming, lemmatization, and part-of-speech tagging to process text data.
  2. Implement and evaluate machine learning models for tasks like sentiment analysis, text classification, and named entity recognition using Python and NLP libraries.
  3. Develop proficiency in advanced NLP methods including neural networks, sequence models like RNNs and LSTMs, and transformers for complex tasks such as machine translation and question answering.
  4. Critically assess the ethical implications of NLP applications and their impact on privacy and bias in language models.
  5. Design and execute NLP projects from conception to implementation, demonstrating the ability to handle real-world data and derive actionable insights.

Course Benefits

  • Flexibility: Learn at your own pace with a fully online format that fits your schedule.
  • Expert Instruction: Gain insights from instructors who are active researchers and experienced practitioners in the field of NLP.
  • Career Advancement: Prepare for roles such as NLP Engineer, Data Scientist, AI Specialist, and more, with practical skills that are highly sought after in the industry.
  • Networking Opportunities: Connect with peers and professionals through forums and group projects, expanding your professional network.
  • Certification: Earn a recognized certificate that validates your expertise in Natural Language Processing, enhancing your professional credibility.

Testimonials

“Completing the NLP Certificate Online not only deepened my understanding of language processing but also opened up numerous career opportunities for me in AI and machine learning.” – Jasmine DiMarco, Graduate

“The course’s practical approach, with hands-on projects and real-world datasets, made the learning process both challenging and incredibly rewarding.” – Billy Moran, Graduate

Course Outline

Module 1: Introduction to Natural Language Processing

  • Overview of NLP and its applications
  • Basic text processing techniques

Module 2: Machine Learning for NLP

  • Supervised and unsupervised learning models
  • Sentiment analysis and text classification

Module 3: Deep Learning in NLP

  • Introduction to neural networks
  • Sequence models: RNNs, LSTMs, and GRUs

Module 4: Advanced NLP Techniques

  • Transformers and attention mechanisms
  • Machine translation and question answering systems

Module 5: Ethical Considerations in NLP

  • Bias and fairness in language models
  • Privacy concerns and data security

Each module includes video lectures, reading assignments, hands-on labs, and quizzes to assess understanding. The capstone project allows students to apply what they have learned to a real-world problem, culminating in a peer-reviewed presentation that showcases their expertise in Natural Language Processing.

Glossary of Terms for Natural Language Processing (NLP)

1. Natural Language Processing (NLP)

  • A subfield of artificial intelligence that focuses on the interaction between computers and human language. It involves programming computers to process and analyze large volumes of natural language data.

2. Tokenization

  • The process of breaking down text into smaller units, such as words or phrases, to facilitate analysis and processing.

3. Normalization

  • The standardization of text by converting it to a consistent format, including tasks like changing the case, removing punctuation, and expanding contractions.

4. Stemming

  • The reduction of words to their base or root form by removing affixes, such as prefixes and suffixes.

5. Lemmatisation

  • Determining a word’s canonical form or lemma based on its meaning and context.

6. Corpus

  • A collection of texts used for linguistic analysis and NLP research, often categorized by language, domain, or theme.

7. Stop Words

  • Common words that are filtered out before processing text, as they typically carry little semantic meaning.

8. Parts-of-speech (POS) Tagging

  • The assignment of grammatical tags to words in a sentence, indicating their syntactic category (e.g., noun, verb, adjective).

9. Statistical Language Modeling

  • Building models that estimate the likelihood of language sequences, enabling tasks like text generation and prediction.

10. Bag of Words

  • A representation model that focuses on the frequency of word occurrences in a text, disregarding grammar and word order.

11. Named Entity Recognition (NER)

  • A text analysis technique used in NLP to automatically extract entities (one or more words that identify a concept) from a text and classify them.

12. Natural Language Generation (NLG)

  • Focuses on text generation, or the construction of text in English or other languages, by a machine and based on a given dataset.

13. Natural Language Understanding (NLU)

  • A subset of NLP focused on the actual computer comprehension of processed and analyzed unstructured text via semantics.

14. Deep Learning

  • A subset of machine learning that simulates human neuron systems through using artificial neural networks with multiple layers to extract higher-level features from data and achieve more complex tasks.

15. Machine Learning (ML)

  • An application of Artificial Intelligence that empowers systems to learn from extensive datasets and tackle specific challenges, operating using computer algorithms that automatically enhance their efficiency through experiential learning.

16. Supervised Learning

  • A type of machine learning in which the model is trained on a labeled dataset, with input-output pairs provided, to learn to make predictions on unseen data.

17. Unsupervised Learning

  • A machine learning technique where the model learns to identify patterns and structures from data without any labels provided.

18. Reinforcement Learning

  • A branch of machine learning focused on guiding intelligent agents to make decisions in an environment, aiming to maximize cumulative rewards.

19. Semantic Analysis

  • The branch of natural language processing that aims to “understand” the meaning of a text, albeit in a less rich manner than human understanding.

20. Chunking (Terminological Extraction)

  • The task of identifying groups of words that form useful expressions, recognizing multi-word expressions that have different meanings than their separate components.

This glossary provides a foundational understanding of key terms used in the field of Natural Language Processing and related disciplines.