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 Duration 14 hours

Course Outline

Foundational Principles of Natural Language Processing

  • Defining Natural Language Processing capabilities.
  • The strategic value of NLP in contemporary artificial intelligence frameworks.
  • Evaluation of standard toolkits including NLTK, SpaCy, and Hugging Face.

Methodologies for Text Data Preparation

  • Execution of tokenization processes and removal of non-essential terms.
  • Application of stemming and lemmatization algorithms.
  • Implementation of text normalization standards.

Sentiment Analysis Frameworks

  • Overview of sentiment analysis methodologies.
  • Execution of sentiment evaluation using the NLTK library.
  • Leveraging SpaCy for sophisticated sentiment interpretation.

Advanced Natural Language Processing Techniques

  • Identification of named entities (NER).
  • Application of text classification models.
  • Utilization of pre-trained models for language modeling.

Utilizing Google Colab Infrastructure

  • Orientation to the Google Colab operational environment.
  • Configuration and administration of NLP workflows within Colab.
  • Facilitation of collaborative NLP development tasks.

Application of NLP in Operational Contexts

  • Implementation of NLP solutions in healthcare, financial services, and customer service domains.
  • Development of interactive chatbots and virtual assistants.
  • Analysis of emerging trends in NLP research and development.

Conclusions and Recommended Subsequent Actions

Requirements

  • Foundational knowledge of natural language processing principles.
  • Proficiency in Python programming.
  • Practical experience with Jupyter Notebooks or comparable development environments.

Intended Audience

  • Data scientists.
  • Developers with demonstrated experience in Python.
  • Professionals interested in artificial intelligence.

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