课程信息

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中级
完成时间大约为7 小时
英语(English)
字幕:英语(English)

您将学到的内容有

  • Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).

  • Evaluate the performance of regressors / classifiers using the above measures.

  • Understand the difference between training/testing performance, and generalizability.

  • Understand techniques to avoid overfitting and achieve good generalization performance.

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。
可灵活调整截止日期
根据您的日程表重置截止日期。
中级
完成时间大约为7 小时
英语(English)
字幕:英语(English)

提供方

加州大学圣地亚哥分校 徽标

加州大学圣地亚哥分校

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 2 小时

Week 1: Diagnostics for Data

完成时间为 2 小时
6 个视频 (总计 49 分钟), 4 个阅读材料, 3 个测验
6 个视频
Motivation Behind the MSE8分钟
Regression Diagnostics: MSE and R²6分钟
Over- and Under-Fitting6分钟
Classification Diagnostics: Accuracy and Error11分钟
Classification Diagnostics: Precision and Recall12分钟
4 个阅读材料
Syllabus10分钟
Setting Up Your System10分钟
(Optional) Additional Resources and Recommended Readings10分钟
Course Materials10分钟
3 个练习
Review: Regression Diagnostics8分钟
Review: Classification Diagnostics4分钟
Diagnostics for Data30分钟
2

2

完成时间为 2 小时

Week 2: Codebases, Regularization, and Evaluating a Model

完成时间为 2 小时
4 个视频 (总计 35 分钟)
4 个视频
Model Complexity and Regularization10分钟
Adding a Regularizer to our Model, and Evaluating the Regularized Model8分钟
Evaluating Classifiers for Ranking4分钟
4 个练习
Review: Setting Up a Codebase2分钟
Review: Regularization5分钟
Review: Evaluating a Model5分钟
Codebases, Regularization, and Evaluating a Model45分钟
3

3

完成时间为 1 小时

Week 3: Validation and Pipelines

完成时间为 1 小时
4 个视频 (总计 24 分钟)
4 个视频
Validation5分钟
“Theorems” About Training, Testing, and Validation8分钟
Implementing a Regularization Pipeline in Python5分钟
Guidelines on the Implementation of Predictive Pipelines5分钟
3 个练习
Review: Validation4分钟
Review: Predictive Pipelines6分钟
Predictive Pipelines20分钟
4

4

完成时间为 2 小时

Final Project

完成时间为 2 小时
2 个阅读材料
2 个阅读材料
Project Description10分钟
Where to Find Datasets10分钟

审阅

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关于 Python Data Products for Predictive Analytics 专项课程

Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego. This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills. Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets....
Python Data Products for Predictive Analytics

常见问题

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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