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上课地点：【上海】：同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】：电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】：北京中山学院/福鑫大楼 【南京分部】：金港大厦(和燕路) 【武汉分部】：佳源大厦（高新二路） 【成都分部】：领馆区1号（中和大道） 【沈阳分部】：沈阳理工大学/六宅臻品 【郑州分部】：郑州大学/锦华大厦 【石家庄分部】：河北科技大学/瑞景大厦 【广州分部】：广粮大厦 【西安分部】：协同大厦
最近开课时间(周末班/连续班/晚班）：2020年3月16日 |
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课程大纲 |
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- Cloudera Introduction to Data Science: Building Re
Cloudera Introduction to Data Science: Building Recommender Systems培训
培训大纲
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: Naïve 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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