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Course Outline
- Distributed Systems under Big Data
- Data Mining Methods (Training Single-Machine Models + Distributed Prediction: Traditional Machine Learning Algorithms + MapReduce Distributed Prediction)
- Apache Spark MLlib
- Recommendations and Precision Advertising:
- Natural Language Components
- Text Clustering, Text Classification (Labeling), and Synonyms
- User Profile Reconstruction and Tagging Systems
- Strategies for Recommendation Algorithms
- Inter-class Lift, Intra-class Lift, and Precision Optimization
- Building a Closed Loop for Recommendation Algorithms
- Logistic Regression, RankingSVM
- Feature Extraction: (Automated Feature Extraction via 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 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.
