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Course Outline
- Distributed Systems under Big Data
- Data mining methods (training single models + distributed predictions: traditional machine learning algorithms + MapReduce distributed predictions)
- Apache Spark MLlib
- Recommendations and Precision Ad Targeting:
- Aspects of Natural Language
- Text clustering, text classification (labelling), and synonyms
- User profile reconstruction and labelling systems
- Strategies for recommendation algorithms
- Inter-class lift, intra-class lift, and how to achieve precision
- How to construct a closed loop for recommendation algorithms
- Logistic Regression, RankingSVM
- Feature Recognition: (Automatic feature extraction via deep learning and graphics)
- Natural Language
- Chinese word segmentation
- Topic modelling (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.
