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Course Outline

  1. Distributed under Big Data
    1. Data mining methods (training single model + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction,)
    2. Apache Spark MLlib
  2. Recommendation and precise ad targeting:
    1. Parts of natural language
    2. Text clustering, text classification (labels), synonyms
    3. User profile recovery, label system
    4. Strategies for recommendation algorithms
    5. Lift between categories, lift within categories, how to be precise
    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 model (text clustering)
    3. Text classification
    4. Extract keywords
    5. Semantic analysis semantic parser, word2vec to word vector
    6. RNN Long short-term memory (TSTM) Architecture

Requirements

There are no specific requirements to participate in this course.

 21 Hours

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