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Course Outline

  1. Distributed Computing 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. Part of Natural Language
    2. Text Clustering, Text Classification (Labels), Synonyms
    3. User Profile Restoration, Tag Systems
    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 Automatic Feature Recognition in Graphs)
  5. Natural Language Processing
    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 (LSTM) Architecture

Requirements

There are no specific prerequisites for participating in this course.

 21 Hours

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