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Course Outline
Introduction to NLP
- Defining Natural Language Processing
- The significance of NLP in contemporary AI applications
- Leading libraries for NLP: NLTK, SpaCy, Hugging Face
Text Preprocessing Techniques
- Tokenization and removal of stop words
- Stemming and lemmatization
- Methods for text normalization
Sentiment Analysis
- Overview of sentiment analysis
- Implementing sentiment analysis with NLTK
- Leveraging SpaCy for advanced sentiment analysis
Advanced NLP Techniques
- Named entity recognition (NER)
- Text classification
- Language modelling with 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 implementation in healthcare, finance, and customer support sectors
- Utilizing 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
- Practical experience with Jupyter Notebooks or comparable environments
Audience
- Data scientists
- Developers with Python expertise
- AI enthusiasts
14 Hours
