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Course Outline
- Distributed Computing under Big Data
- Data mining methods (training single models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction,)
- Apache Spark MLlib
- Recommendation and precise advertising:
- Partial natural language processing
- Text clustering, text classification (labeling), synonyms
- User profile reconstruction, tagging systems
- Strategies for recommendation algorithms
- Lift between classes, lift within classes, how to achieve precision
- How to build a closed loop for recommendation algorithms
- Logistic Regression, RankingSVM,
- Feature identification: (automatic feature recognition with deep learning and graphs)
- Natural Language
- 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 prerequisites 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.
