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Duration 21 hours
Course Outline
- Distributed systems under big data
- Data mining methods (training single-machine + distributed predictions: traditional machine learning algorithms + MapReduce distributed predictions,)
- Apache Spark MLlib
- Recommendations and precise ad placement:
- Parts of natural language
- Text clustering, text classification (labels), synonyms
- User profile reconstruction, label systems
- 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 graphs)
- 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 (LSTM) Architecture
Requirements
There are no specific requirements to join this course.
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
