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Course Outline
- Distribution under Big Data
- Data mining methods (training on single models + distributed predictions: traditional machine learning algorithms + MapReduce distributed predictions)
- Apache Spark MLlib
- Recommendations and Targeted Advertising:
- Components of Natural Language Processing
- Text clustering, text classification (tagging), synonyms
- User profile reconstruction, tagging systems
- Strategies for recommendation algorithms
- Lift between categories, lift within categories, achieving precision
- How to build a closed loop for recommendation algorithms
- Logistic Regression, RankingSVM
- Feature recognition: (automatic feature recognition in deep learning and shapes)
- Natural Language
- Chinese word segmentation
- Topic modeling (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 joining this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
