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Course Outline

  1. Distributed Systems in Big Data
    1. Data Mining Methods (Training Single-Model + Distributed Prediction: Traditional Machine Learning Algorithms + MapReduce Distributed Prediction)
    2. Apache Spark MLlib
  2. Recommendation and Precise Ad Targeting:
    1. Aspects of Natural Language Processing
    2. Text Clustering, Text Classification (Tagging), and Synonyms
    3. User Profile Restoration and Tagging Systems
    4. Strategies for Recommendation Algorithms
    5. Inter-class Lift, Intra-class Lift, and Achieving Precision
    6. Constructing the Closed Loop of Recommendation Algorithms
  3. Logistic Regression and RankingSVM
  4. Feature Recognition: (Automated Feature Recognition via Deep Learning and Graph Techniques)
  5. Natural Language Processing
    1. Chinese Word Segmentation
    2. Topic Models (Text Clustering)
    3. Text Classification
    4. Keyword Extraction
    5. Semantic Analysis: Semantic Parsers and 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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