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Course Outline
- Distributed Processing in Big Data
- Data Mining Methods (Training Single Machines + Distributed Prediction: Traditional Machine Learning Algorithms + MapReduce Distributed Prediction)
- Apache Spark MLlib
- Recommendations and Precision Advertising:
- Natural Language Components
- Text Clustering, Text Classification (Labeling), and Synonyms
- User Profile Reconstruction and Tag Systems
- Strategies for Recommendation Algorithms
- Lift between Categories, Lift within Categories, and Precision Optimization
- Building a Closed-Loop for Recommendation Algorithms
- Logistic Regression and RankingSVM
- Feature Extraction: (Automatic Feature Recognition via Deep Learning and Graphs)
- Natural Language Processing
- Chinese Word Segmentation
- Topic Models (Text Clustering)
- Text Classification
- Keyword Extraction
- Semantic Analysis: Semantic Parsers and Word2Vec to Word Vectors
- RNN Long Short-Term Memory (LSTM) Architecture
Requirements
There are no specific prerequisites for enrolling in this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
