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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, Hugging Face
Text Preprocessing Techniques
- Tokenization and removal of stop words
- Stemming and lemmatization
- Text normalization techniques
Sentiment Analysis
- Overview of sentiment analysis
- Executing sentiment analysis with NLTK
- Utilising SpaCy for advanced sentiment analysis
Advanced NLP Techniques
- Named entity recognition (NER)
- Text classification
- Language modeling with 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 in Colab
Real-World Applications of NLP
- Applications of NLP in healthcare, finance, and customer support
- Leveraging NLP for chatbots and virtual assistants
- Emerging trends in NLP research
Summary and Next Steps
Requirements
- Foundational understanding of natural language processing concepts
- Proficiency in Python programming
- Experience with Jupyter Notebooks or comparable environments
Audience
- Data scientists
- Developers with Python experience
- AI enthusiasts
14 Hours
