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Course Outline

  1. Distributed under Big Data
    1. Data mining methods (training single models + distributed predictions: traditional machine learning algorithms + MapReduce distributed predictions)
    2. Apache Spark MLlib
  2. Recommendations and Precision Ad Placement:
    1. Natural language parts
    2. Text clustering, text classification (labels), synonyms
    3. User profile restoration, tag system
    4. Strategies for recommendation algorithms
    5. Lift between categories, lift within categories, how to achieve precision
    6. How to build a closed-loop recommendation algorithm
  3. Logistic Regression, RankingSVM
  4. Feature Recognition: (Automatic feature recognition in deep learning and 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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