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Course Outline

  1. Distributed Systems in 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 Targeted Advertising:
    1. Natural Language Processing components
    2. Text clustering, text classification (labels), synonyms
    3. User profile recovery, label systems
    4. Strategies for recommendation algorithms
    5. Lift between classes, intra-class lift, and how to achieve precision
    6. How to build a closed loop for recommendation algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature Recognition: (Deep Learning and Graph-based Automatic Feature Recognition)
  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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