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Duration 14 hours
Course Outline
Introduction to NLP
- Defining Natural Language Processing
- The role of NLP in contemporary AI applications
- Key NLP libraries: NLTK, SpaCy, and Hugging Face
Text Preprocessing Methods
- Tokenization and filtering stop words
- Applying stemming and lemmatization
- Techniques for text normalization
Sentiment Analysis
- Overview of sentiment analysis concepts
- Implementing sentiment analysis with NLTK
- Advanced sentiment analysis using SpaCy
Advanced NLP Techniques
- Named Entity Recognition (NER)
- Text classification strategies
- Language modeling with pre-trained models
Working with Google Colab
- Navigating the Google Colab environment
- Configuring and managing NLP projects in Colab
- Collaborative NLP development in Colab
Practical Applications of NLP
- NLP in healthcare, finance, and customer service
- Developing chatbots and virtual assistants with NLP
- Emerging trends in NLP research
Recap and Future Steps
Requirements
- Foundational knowledge of natural language processing concepts
- Proficiency in Python programming
- Prior experience with Jupyter Notebooks or comparable development environments
Target Audience
- Data scientists
- Developers with a background in Python
- Enthusiasts of Artificial Intelligence