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Duration 14 hours
Course Outline
Introduction to NLP
- Defining Natural Language Processing
- The significance of NLP in modern AI applications
- Leading NLP libraries: NLTK, SpaCy, Hugging Face
Text Preprocessing Techniques
- Tokenization and removal of stop words
- Stemming and lemmatization
- Text normalization methods
Sentiment Analysis
- Overview of sentiment analysis
- Conducting sentiment analysis with NLTK
- Lleveraging SpaCy for advanced sentiment analysis
Advanced NLP Techniques
- Named entity recognition (NER)
- Text classification
- Language modeling using pre-trained models
Working with Google Colab
- Overview of the Google Colab environment
- Setting up and managing NLP projects in Colab
- Collaborating on NLP tasks within Colab
Real-World Applications of NLP
- NLP implementations in healthcare, finance, and customer support
- Utilizing 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
- Experience working with Jupyter Notebooks or similar platforms
Target Audience
- Data scientists
- Developers with Python experience
- AI enthusiasts
