 班級規(guī)模及環(huán)境--熱線:4008699035 手機(jī):15921673576( 微信同號) |
|
每期人數(shù)限3到5人。 |
| 上課時間和地點 |
上課地點：【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
最近開課時間(周末班/連續(xù)班/晚班）：2020年3月16日 |
| 實驗設(shè)備 |
☆資深工程師授課
☆注重質(zhì)量
☆邊講邊練
☆合格學(xué)員免費推薦工作
★實驗設(shè)備請點擊這兒查看★ |
| 質(zhì)量保障 |
1、培訓(xùn)過程中，如有部分內(nèi)容理解不透或消化不好，可免費在以后培訓(xùn)班中重聽；
2、培訓(xùn)結(jié)束后,授課老師留給學(xué)員聯(lián)系方式,保障培訓(xùn)效果,免費提供課后技術(shù)支持。
3、培訓(xùn)合格學(xué)員可享受免費推薦就業(yè)機(jī)會。 |
課程大綱 |
| |
Cloudera Introduction to Data Science: Building Re
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
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
???
?
?
?
?
?
|
首頁 課程表 在線聊 報名 講師 品牌 QQ聊 活動 就業(yè)