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Course Outline
Introduction to NLP
- Defining Natural Language Processing.
- The significance of NLP in contemporary AI applications.
- Leading NLP libraries: NLTK, SpaCy, and Hugging Face.
Text Preprocessing Techniques
- Tokenization and the removal of stop words.
- Stemming and lemmatization processes.
- Techniques for text normalization.
Sentiment Analysis
- Overview of sentiment analysis.
- Conducting sentiment analysis with NLTK.
- Utilizing SpaCy for advanced sentiment analysis.
Advanced NLP Techniques
- Named entity recognition (NER).
- Text classification methods.
- Language modeling using pre-trained models.
Working with Google Colab
- Overview of the Google Colab environment.
- Setting up and managing NLP projects within Colab.
- Collaborative execution of NLP tasks in Colab.
Real-World Applications of NLP
- NLP implementations in healthcare, finance, and customer support.
- Deploying 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.
Target Audience
- Data scientists.
- Developers with Python expertise.
- AI enthusiasts.
14 Hours
