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Course Outline
Introduction to NLP
- What is Natural Language Processing?
- The significance of NLP in contemporary AI applications.
- Prominent libraries for NLP: NLTK, SpaCy, and Hugging Face.
Text Preprocessing Techniques
- Tokenization and the removal of stop words.
- Stemming and lemmatization.
- Techniques for text normalization.
Sentiment Analysis
- An introduction to sentiment analysis.
- Executing sentiment analysis with NLTK.
- Utilizing SpaCy for advanced sentiment analysis.
Advanced NLP Techniques
- Named entity recognition (NER).
- Text classification.
- Language modeling using pre-trained models.
Working with Google Colab
- Overview of the Google Colab environment.
- Setting up and managing NLP projects in Colab.
- Collaborating on NLP tasks within Colab.
Real-World Applications of NLP
- Applications of NLP in healthcare, finance, and customer support.
- Utilizing NLP for chatbots and virtual assistants.
- Emerging trends in NLP research.
Summary and Next Steps
Requirements
- A foundational understanding of natural language processing principles.
- Familiarity with Python programming.
- Experience working with Jupyter Notebooks or comparable environments.
Audience
- Data scientists.
- Developers proficient in Python.
- Artificial intelligence enthusiasts.
14 Hours
