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Course Outline
- Distributed under Big Data
- Data mining methods (training single model + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction,)
- Apache Spark MLlib
- Recommendation and precise ad targeting:
- Parts of natural language
- Text clustering, text classification (labels), synonyms
- User profile recovery, label system
- Strategies for recommendation algorithms
- Lift between categories, lift within categories, how to be precise
- How to build a closed loop for recommendation algorithms
- Logistic Regression, RankingSVM,
- Feature recognition: (automatic feature recognition in deep learning and graphs)
- Natural Language
- Chinese word segmentation
- Topic model (text clustering)
- Text classification
- Extract keywords
- Semantic analysis semantic parser, word2vec to word vector
- RNN Long short-term memory (TSTM) Architecture
Requirements
There are no specific requirements to participate in this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
