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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. Recommendations and Precision Ad Targeting:
    1. Partial Natural Language
    2. Text clustering, text classification (tags), synonyms
    3. User profile restoration, tag system
    4. Strategy of 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: (Deep Learning and automatic feature recognition of graphs)
  5. Natural Language
    1. Chinese word segmentation
    2. Topic Model (text clustering)
    3. Text classification
    4. Extracting keywords
    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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