Section outline

    • ·         Micro-Topic 16.1: From Words to Numbers – How to Represent Text (Goal: Grasp why and how we turn text into numeric form for machine learning.)

    • L16 MT1 FREE content SCORM package
      • Micro-Topic 16.2: Word Embeddings – Giving Meaning to Word Vectors (Goal: Understand what word embeddings are and why they are useful.)
    • L16 MT2 FREE content SCORM package
      • Micro-Topic 16.3: Learning Word Vectors – Word2Vec and Friends (Goal: Understand at a high level how word embeddings are learned from large corpora.)
    • L16 MT3 FREE content SCORM package
      • Micro-Topic 16.4: Using Embeddings for Text Classification (Goal: Learn how word vectors are utilized in a simple text categorization model.)
    • L16 MT4 FREE content SCORM package
      • Micro-Topic 16.5: Example – Categorizing Movie Reviews by Sentiment (Goal: Provide a concrete example of text categorization using embeddings, tying everything together.)
    • L16 MT5 FREE content SCORM package