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 Duration 21 hours

Course Outline

  1. Distributed Systems in Big Data
    1. Data mining methods (training single-node models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendations and Precision Advertising:
    1. Components of Natural Language Processing
    2. Text clustering, text classification (labeling), and synonyms
    3. User profile reconstruction and label systems
    4. Strategies for recommendation algorithms
    5. Intra-class lift, inter-class lift, and achieving precision
    6. Building a closed loop for recommendation algorithms
  3. Logistic Regression, RankingSVM
  4. Feature extraction: (Automatic feature extraction from 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 parsers, Word2Vec to word vectors
    6. RNN Long Short-Term Memory (LSTM) Architecture

Requirements

There are no specific requirements for joining this course.

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