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Course Outline
Introduction to NLP
- What is Natural Language Processing?
- The significance of NLP in contemporary AI applications.
- Leading libraries for NLP: NLTK, SpaCy, and Hugging Face.
Text Preprocessing Techniques
- Tokenization and removal of stop words.
- Stemming and lemmatization.
- Text normalization strategies.
Sentiment Analysis
- Overview of 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.
- Establishing and managing NLP projects in Colab.
- Collaborating on NLP tasks within Colab.
Real-World Applications of NLP
- NLP applications in healthcare, finance, and customer support.
- Leveraging NLP for chatbots and virtual assistants.
- Emerging trends in NLP research.
Summary and Next Steps
Requirements
- Foundational knowledge of natural language processing concepts.
- Proficiency in Python programming.
- Previous experience with Jupyter Notebooks or comparable environments.
Target Audience
- Data scientists.
- Developers with Python expertise.
- Artificial intelligence enthusiasts.
14 Hours
