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Duration 14 hours
Course Outline
Introduction to NLP
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Defining Natural Language Processing?
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The significance of NLP in contemporary AI applications.
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Leading NLP libraries: NLTK, SpaCy, and Hugging Face.
Text Preprocessing Techniques
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Tokenization and removal of stop words.
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Stemming and lemmatization.
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Techniques for text normalization.
Sentiment Analysis
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Overview of sentiment analysis.
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Conducting sentiment analysis with NLTK.
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Leveraging SpaCy for advanced sentiment analysis.
Advanced NLP Techniques
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Named Entity Recognition (NER).
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Text classification.
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Language modeling with pre-trained models.
Working with Google Colab
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Overview of the Google Colab environment.
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Setting up and managing NLP projects in Colab.
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Collaborating on NLP tasks within Colab.
Real-World Applications of NLP
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NLP usage in healthcare, finance, and customer support.
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Implementing NLP for chatbots and virtual assistants.
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Emerging trends in NLP research.
Summary and Next Steps
Requirements
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Foundational knowledge of natural language processing concepts.
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Familiarity with Python programming.
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Prior experience with Jupyter Notebooks or comparable environments.
Target Audience
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Data scientists.
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Developers with Python expertise.
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Enthusiasts of Artificial Intelligence.
