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Duration 21 hours
Course Outline
- Distributed Systems for Big Data
- Data Mining Methods (Training on Single Model + Distributed Prediction: Traditional Machine Learning Algorithms + MapReduce Distributed Prediction)
- Apache Spark MLlib
- Recommendation and Precise Advertising:
- Components of Natural Language
- Text Clustering, Text Classification (Tagging), Synonyms
- User Profile Reconstruction, Tag System
- Strategies for Recommendation Algorithms
- Lift between classes, Lift within classes, and methods for precision
- How to build a closed loop for recommendation algorithms
- Logistic Regression, RankingSVM
- Feature Recognition: (Automatic feature recognition for deep learning and graphics)
- 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 this course.
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
