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Course Outline
- Distributed systems under Big Data
- Data mining methods (training on single-machine models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction,)
- Apache Spark MLlib
- Recommendations and precision advertising:
- Aspects of natural language
- Text clustering, text classification (labeling), synonyms
- User profile reconstruction, label system
- 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 extraction: (Automatic feature extraction for deep learning and graphics)
- 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 (TSTM) Architecture
Requirements
There are no specific requirements for joining this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
