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Course Outline
- Distributed Systems in Big Data
- Data Mining Methods (Training single-node models + Distributed prediction: Traditional machine learning algorithms + MapReduce distributed prediction)
- Apache Spark MLlib
- Recommendations and Targeted Advertising:
- Components of Natural Language Processing
- 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 Metrics
- Building Closed-Loops for Recommendation Algorithms
- Logistic Regression, RankingSVM
- Feature Recognition: (Automatic Feature Extraction in Deep Learning and Graphs)
- Natural Language Processing
- Chinese Word Segmentation
- Topic Modeling (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 required to enroll in this course.
21 Hours
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
