In this project, you will:

• Automate content segmentation using word embeddings and clustering techniques.
  • Aim: Categorize text into meaningful segments without manual intervention.
  • Methodology:
    • Utilize contextual word embeddings, such as BERT, to transform words into vectors in a continuous vector space.
    • Capture semantic relationships between words for understanding context and meaning.
    • Apply dimensionality reduction and a clustering algorithm (e.g., K-means or Agglomerative clustering) to group word vectors into clusters.
    • Each cluster will represent a distinct segment of content, signifying a topic or theme.
  • Benefits:
    • Provides an automated approach to content segmentation.
    • Offers a scalable solution for organizing large volumes of text data.

• Content recommendation systems in educational contexts.
  • Handles a massive amount of content
  • Provides customized content recommendations
  • Challenges addressed:
    • Collaborative filtering may favor popular courses, limiting diversity.
    • Content-based filtering struggles with diverse learning styles and interests, making personalized recommendations difficult.
  • Our approach:
    • Enhance diversity and personalization of content recommendations.
    • Address challenges by leveraging word embeddings and clustering.
  • Benefits for students:
    • Tailors educational materials to individual learning needs
    • Helps engage with the most relevant resources
    • Enhances the overall learning experience
    • Leads to improved educational outcomes
    • Creates more efficient learning pathways

• For organizations managing vast amounts of unstructured text data:
  • Customer feedback
  • News articles
  • Social media posts
• By automating content segmentation:
  • Gain insights into prevalent themes
  • Detect emerging trends
  • Streamline content management processes
• Advantages:
  • Reduces time and effort required for manual content analysis
  • Enables quicker decision-making
  • Allows more efficient resource allocation
• Improves business strategies by:
  • Identifying patterns and topics within text data
  • Enhancing targeted marketing strategies
  • Increasing customer satisfaction by addressing common issues
  • Revealing underlying sentiments and opinions

Who should take this guided project

What you'll learn

• Implement advanced word embeddings to represent text data effectively.
• Apply clustering techniques such as K-means and Agglomerative clustering to identify patterns and group similar content.
• Utilize PCA for reducing vector dimensions, making data more manageable and insights more accessible.
• Analyze and interpret text datasets to uncover underlying themes and trends.

What you'll need

• A basic understanding of Python programming.
• Access to a modern web browser like Chrome, Edge, Firefox, Internet Explorer, or Safari for optimal performance.
• Note that the IBM Skills Network Labs environment includes pre-installed tools like Docker to simplify your setup process.