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Course Outline
Introduction to NLP
- Defining Natural Language Processing
- The role of NLP in contemporary AI applications
- Key libraries for NLP: NLTK, SpaCy, and Hugging Face
Text Preprocessing Methods
- Tokenization and stop word removal
- Stemming and lemmatization processes
- Techniques for text normalization
Sentiment Analysis
- Overview of sentiment analysis methodologies
- Implementing sentiment analysis with NLTK
- Leveraging SpaCy for advanced sentiment tasks
Advanced NLP Techniques
- Named Entity Recognition (NER)
- Text classification strategies
- Language modeling using pre-trained models
Utilizing Google Colab
- Overview of the Google Colab environment
- Configuring and managing NLP projects in Colab
- Collaborative workflows for NLP tasks in Colab
Real-World NLP Applications
- NLP in healthcare, finance, and customer service
- Developing chatbots and virtual assistants with NLP
- Emerging trends in NLP research
Summary and Future Directions
Requirements
- Foundational knowledge of natural language processing principles
- Proficiency in Python programming
- Prior experience with Jupyter Notebooks or comparable environments
Target Audience
- Data scientists
- Developers with Python expertise
- AI enthusiasts
14 Hours
