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Course Outline
- Distributed Computing under Big Data
- Data mining techniques (Training single-machine models + Distributed predictions: Traditional machine learning algorithms + MapReduce distributed prediction)
- Apache Spark MLlib
- Recommendations and Precision Advertising:
- Components of Natural Language Processing
- Text clustering, Text classification (labels), Synonyms
- User profile reconstruction, Label systems
- Strategies for recommendation algorithms
- Lift between classes, Lift within classes, and how to achieve precision
- How to build a closed loop for recommendation algorithms
- Logistic Regression, RankingSVM
- Feature Extraction: (Automatic feature extraction for deep learning and graphs)
- Natural Language Processing
- Chinese Word Segmentation
- Topic models (Text clustering)
- Text classification
- Keyword extraction
- Semantic analysis: Semantic parser, Word2Vec to Word Vectors
- RNN Long Short-Term Memory (LSTM) Architecture
Requirements
There are no specific requirements for participating in this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
