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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 targeted advertising:
    1. Parsing of natural language
    2. Text clustering, text classification (labeling), synonym
    3. User profile restoration, label system
    4. Strategies for recommendation algorithms
    5. Lift between classes, lift within classes, how to be precise
    6. How to build a closed loop for recommendation algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature recognition: (Automatic feature recognition with deep learning and graphs)
  5. Natural Language
    1. Chinese word segmentation
    2. Topic models (text clustering)
    3. Text classification
    4. Extract keywords
    5. Semantic analysis, semantic parser, word2vec to word vectors
    6. RNN Long short-term memory (LSTM) Architecture
 21 Hours

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