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Course Outline
- Distributed Systems in the Era of Big Data
- Data mining methods (training standalone models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
- Apache Spark MLlib
- Recommendations and Precision Advertising:
- Components of Natural Language
- Text clustering, text classification (labeling), and synonyms
- User profile reconstruction and labeling systems
- Strategies for recommendation algorithms
- Inter-class lift, intra-class lift, and how to ensure precision
- How to build a closed loop for recommendation algorithms
- Logic Regression, RankingSVM,
- Feature extraction: (automatic feature extraction for deep learning and graph structures)
- Natural Language
- Chinese word segmentation
- Topic models (text clustering)
- Text classification
- Keyword extraction
- Semantic analysis: semantic parsers, Word2Vec to word vectors
- RNN Long Short-Term Memory (LSTM) Architecture
Requirements
There are no specific prerequisites for joining this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
