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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. Recommendations and Precise Ad Targeting:
    1. Aspects of Natural Language
    2. Text Clustering, Text Classification (Labels), Synonyms
    3. User Profile Reconstruction, Tag Systems
    4. Strategies for Recommendation Algorithms
    5. Lift Between Classes, Lift Within Classes, How to Be Precise
    6. How to Build a Closed Loop for Recommendation Algorithms
  3. Logistic Regression, RankingSVM,
  4. Feature Identification: (Automatic Feature Identification Through Deep Learning and Graphs)
  5. Natural Language
    1. Chinese Word Segmentation
    2. Topic Models (Text Clustering)
    3. Text Classification
    4. Keyword Extraction
    5. Semantic Analysis (Sementic Parser, Word2Vec to Word Vectors)
    6. RNN Long short-term memory (TSTM) Architecture
 21 Hours

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