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Course Outline

  1. Distributed 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. Partial Natural Language Processing
    2. Text clustering, text classification (labels), synonyms
    3. User profile recovery, label system
    4. Strategies for recommendation algorithms
    5. Lift between classes, intra-class lift, how to achieve precision
    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 Processing
    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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