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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 Methods
- Tokenization and the removal of stop words.
- Techniques for stemming and lemmatization.
- Strategies for text normalization.
Sentiment Analysis
- Fundamentals of sentiment analysis.
- Implementing sentiment analysis with NLTK.
- Leveraging SpaCy for advanced sentiment insights.
Advanced NLP Techniques
- Named entity recognition (NER).
- Text classification methods.
- Language modeling utilizing pre-trained models.
Utilizing Google Colab
- Navigating the Google Colab environment.
- Setting up and managing NLP projects within Colab.
- Collaborative workflows for NLP tasks in Colab.
Real-World NLP Applications
- NLP use cases in healthcare, finance, and customer support.
- Building chatbots and virtual assistants with NLP.
- Emerging trends in NLP research.
Summary and Recommended Next Steps
Requirements
- A foundational understanding of natural language processing concepts.
- Proficiency in Python programming.
- Familiarity with Jupyter Notebooks or comparable environments.
Target Audience
- Data scientists.
- Developers with experience in Python.
- Enthusiasts of AI and machine learning.
14 Hours
