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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 Prediction,)
    2. Apache Spark MLlib
  2. Recommendations and Precise Ad Delivery:
    1. Partial aspects of Natural Language
    2. Text Clustering, Text Classification (Tagging), Synonyms
    3. User Profile Recovery, Tag System
    4. Recommendation Algorithm Strategies
    5. Lift Between Classes, Lift Within Classes, How to Achieve Precision
    6. How to Build a Closed Loop for Recommendation Algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature Recognition: (Deep Learning and Automated Feature Recognition in Graphs)
  5. Natural Language
    1. Chinese Word Segmentation
    2. Topic Models (Text Clustering)
    3. Text Classification
    4. Keyword Extraction
    5. Semantic Analysis: Semantic Parser, from Word2Vec to Word Vectors
    6. RNN Long Short-Term Memory (LSTM) Architecture
 21 Hours

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