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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. Recommendation and Precision Ad Targeting:
    1. Parts of Natural Language
    2. Text Clustering, Text Classification (Labels), Synonyms
    3. User Profile Reconstruction, Label Systems
    4. Strategies for Recommendation Algorithms
    5. Lift Between Classes, Lift Within Classes, How to Achieve Precision
    6. How to Build a Closed Loop for Recommendation Algorithms
  3. Logical Regression, RankingSVM,
  4. Feature Recognition: (Automatic Feature Recognition with Deep Learning and Graphs)
  5. Natural Language
    1. Chinese Word Segmentation
    2. Topic Model (Text Clustering)
    3. Text Classification
    4. Keyword Extraction
    5. Semantic Analysis sementic parser, word2vec to Word Vectors
    6. RNN Long short-term memory (LSTM) Architecture
 21 Hours

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