Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.
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Data Science Fundamentals 专项课程加州大学尔湾分校课程信息
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第 2 门课程（共 4 门）
中级
完成时间大约为4 小时
英语（English）
字幕：英语（English）
您将学到的内容有
The application of predictive modeling to professional and academic work
Applications of classification analysis: decision trees
Applications of regression analysis (linear and logistic)
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 4 门）
中级
完成时间大约为4 小时
英语（English）
字幕：英语（English）
提供方
加州大学尔湾分校
Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.
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完成时间为 1 小时
Predictive Modeling
Welcome to Module 1, Predictive Modeling. In this module we will begin with a comparison of predictive and descriptive analytics, and discuss what can be learned from both. We will also discuss supervised and unsupervised modeling, two foundational models in analytics and machine learning.
完成时间为 1 小时
1 个视频 （总计 3 分钟）, 2 个阅读材料
完成时间为 1 小时
Data Dimensionality and Classification Analysis
Welcome to Module 2, Data Dimensionality and Classification Analysis. In this module we will explore how data can be classified and how decision trees can be leveraged as a fast, easy to use a model that is easy to interpret, explain, and visualize.
完成时间为 1 小时
2 个阅读材料
完成时间为 1 小时
Model Fitting
Welcome to Module 3, Model Fitting. In this module we will explore the concept of model fitting and how creating a generalized model that is able to fit both historical and future data is the ultimate goal. We will also review how a model can be trained or scored to apply to new and unlabeled data.
完成时间为 1 小时
1 个视频 （总计 3 分钟）, 2 个阅读材料
完成时间为 2 小时
Regression Analysis
Welcome to Module 4, Regression Analysis. In this module we will begin with an explanation of regression analytics, a popular technique used by data science professionals to make predictions. We will also discuss how achieving model fit is not a guarantee that a model can help solve a business problem, and how even a good model can sometimes lead to unactionable outcomes.
完成时间为 2 小时
2 个阅读材料
审阅
- 5 stars65%
- 4 stars17.50%
- 3 stars7.50%
- 2 stars5%
- 1 star5%
来自PREDICTIVE MODELING, MODEL FITTING, AND REGRESSION ANALYSIS的热门评论
由 MA 提供Jan 11, 2022
course content is very concise and easy to understand
由 NA 提供Jan 27, 2022
good course to understand the fundamentals of predictive analysis
由 CH 提供Jun 26, 2021
Thank you Very Much I learn a lot of Thing with all kinds of Predative Modeling that I can use.
关于 Data Science Fundamentals 专项课程
This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques. The course also provides an overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations.
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