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Course Outline
- Distributed Processing in Big Data
- Data Mining Techniques (Training single-machine models + Distributed predictions: Traditional Machine Learning algorithms + MapReduce distributed prediction)
- Apache Spark MLlib
- Recommendations and Precision Advertising:
- Components of Natural Language Processing
- Text clustering, text classification (labeling), and synonyms
- User profile reconstruction and the tag system
- Strategies for recommendation algorithms
- Lift between classes, lift within classes, and precision optimisation
- Building a closed loop for recommendation algorithms
- Logistic Regression, RankingSVM
- Feature Extraction: (Automatic feature extraction using Deep Learning and Graphs)
- Natural Language Processing
- Chinese word segmentation
- Topic models (text clustering)
- Text classification
- Keyword extraction
- Semantic analysis: semantic parsers, Word2Vec and word vectors
- RNN Long Short-Term Memory (LSTM) Architecture
Requirements
No specific prerequisites are required for this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
