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Duration 14 hours
Course Outline
Introduction to NLP
- Defining Natural Language Processing.
- The significance of NLP in contemporary AI applications.
- Overview of leading NLP libraries: NLTK, SpaCy, and Hugging Face.
Text Preprocessing Techniques
- Tokenization and the removal of stop words.
- Applying stemming and lemmatization.
- Methods for text normalization.
Sentiment Analysis
- Fundamentals of sentiment analysis.
- Executing sentiment analysis tasks with NLTK.
- Leveraging SpaCy for advanced sentiment analysis.
Advanced NLP Techniques
- Named Entity Recognition (NER).
- Text classification methodologies.
- Language modeling utilizing pre-trained models.
Working with Google Colab
- Navigating the Google Colab environment.
- Configuring and overseeing NLP projects in Colab.
- Facilitating collaboration on NLP tasks within Colab.
Real-World Applications of NLP
- Applications of NLP in healthcare, finance, and customer support.
- Deploying NLP for chatbots and virtual assistants.
- Emerging trends in NLP research.
Summary and Next Steps
Requirements
- A fundamental grasp of natural language processing concepts.
- Proficiency in Python programming.
- Prior experience working with Jupyter Notebooks or comparable development environments.
Target Audience
- Data scientists.
- Developers with established Python skills.
- Enthusiasts of Artificial Intelligence.
