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Duration 21 hours (3 days)
Course Outline
- Distributed Computing in Big Data
- Data mining methods (training single-node models + distributed prediction: traditional machine learning algorithms + MapReduce distributed prediction)
- Apache Spark MLlib
- Recommendation and Precision Advertising:
- Natural Language Components
- Text clustering, text classification (labeling), and synonyms
- User profile restoration and tagging systems
- Strategies for recommendation algorithms
- Lift between classes, lift within classes, and achieving precision
- Building a closed-loop for recommendation algorithms
- Logistic Regression, RankingSVM
- Feature Identification: (Deep learning and automatic feature identification for shapes)
- Natural Language
- Chinese word segmentation
- Topic models (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 joining this course.
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
