Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to NLP
- Defining Natural Language Processing.
- The significance of NLP in contemporary AI applications.
- Leading libraries for NLP: NLTK, SpaCy, and Hugging Face.
Text Preprocessing Techniques
- Tokenization and the removal of stop words.
- Stemming and lemmatization processes.
- Various text normalization techniques.
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 with 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 sectors.
- Leveraging NLP for chatbots and virtual assistants.
- Emerging trends in NLP research.
Summary and Next Steps
Requirements
- A foundational understanding 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 strong background in Python.
- Enthusiasts of Artificial Intelligence.
14 Hours
