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上課地點(diǎn)：【上海】：同濟(jì)大學(xué)(滬西)/新城金郡商務(wù)樓(11號線白銀路站) 【深圳分部】：電影大廈(地鐵一號線大劇院站)/深圳大學(xué)成教院 【北京分部】：北京中山學(xué)院/福鑫大樓 【南京分部】：金港大廈(和燕路) 【武漢分部】：佳源大廈（高新二路） 【成都分部】：領(lǐng)館區(qū)1號（中和大道） 【沈陽分部】：沈陽理工大學(xué)/六宅臻品 【鄭州分部】：鄭州大學(xué)/錦華大廈 【石家莊分部】：河北科技大學(xué)/瑞景大廈 【廣州分部】：廣糧大廈 【西安分部】：協(xié)同大廈
最近開課時(shí)間(周末班/連續(xù)班/晚班）：2020年3月16日 |
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課程大綱 |
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- Cloudera Introduction to Data Science: Building Re
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Cloudera Introduction to Data Science: Building Recommender Systems培訓(xùn)
培訓(xùn)大綱
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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