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Course Outline

  1. Distributed under Big Data
    1. Data mining methods (training single models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendation and precision ad targeting:
    1. Part of speech in natural language
    2. Text clustering, text classification (labels), synonyms
    3. User profile recovery and label systems
    4. Recommendation algorithm strategies
    5. Lift between categories, within-category lift, and how to achieve precision
    6. How to build a closed loop for recommendation algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature recognition: (automatic feature recognition in deep learning and graphs)
  5. Natural Language
    1. Chinese word segmentation
    2. Topic models (text clustering)
    3. Text classification
    4. Keyword extraction
    5. Semantic analysis: semantic parser, Word2Vec to word vectors
    6. RNN Long short-term memory (LSTM) Architecture

Requirements

There are no specific requirements to participate in this course.

 21 Hours

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