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Course Outline

  1. Distributed Systems in the Age of 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 Precision Ad Targeting:
    1. Parts of Natural Language
    2. Text Clustering, Text Classification (Labels), Synonyms
    3. User Profile Restoration, Label Systems
    4. Strategies for Recommendation Algorithms
    5. Lift between Classes, Lift within Classes, and How to Achieve Precision
    6. How to Build a Closed Loop for Recommendation Algorithms
  3. Logical Regression, RankingSVM,
  4. Feature Recognition: (Automatic Feature Recognition in 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: 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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