Cloudera Introduction to Data Science: Building Re
Cloudera Introduction to Data Science: Building Recommender Systems培訓
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1. Data Science
What is Data Science?
Growing Need for Data Science
Role of a Data Scientist
2. Use Cases
Finance
Retail
Advertising
Defense and Intelligence
Telecommunications and Utilities
Healthcare and Pharmaceuticals
3. Project Life Cycle
Steps in the Project Life Cycle
4. Data Acquisition
Where to Source Data
Acquisition Techniques
Evaluating Input Data
Data Formats
Data Quantity
Data Quality
5. Data Transformation
Anonymization
File Format Conversion
Joining Datasets
6. Data Analysis and Statistical Methods
Relationship Between Statistics and Probability
Descriptive Statistics
Inferential Statistics
7. Fundamentals of Machine Learning
Three Cs of Machine Learning
Spotlight: Na?ve Bayes Classifiers
Importance of Data and Algorithms
8. Recommender
What is a Recommender System?
Types of Collaborative Filtering
Limitations of Recommender
9. Systems Fundamental Concepts
10. Apache Mahout
What Apache Mahout is (and is not)
History of Mahout
Availability and Installation
Demonstration: Using Mahout's Item-Based Recommender
11. Implementing Recommenders with Apache Mahout
Similarity Metrics for Binary Preferences
Similarity Metrics for Numeric Preferences
Scoring
12. Experimentation and Evaluation
Measuring Recommender Effectiveness
Designing Effective Experiments
Conducting an Effective Experiment
User Interfaces for Recommenders
13. Production Deployment and Beyond
Deploying to Production
Tips and Techniques for Working at Scale
Summarizing and Visualizing Results
Considerations for Improvement
Next Steps for Recommenders
14. Appendix A: Hadoop
15. Appendix B: Mathematical Formulas
16. Appendix C: Language and Tool Reference
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