课程信息
3,495 次近期查看

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

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中级

完成时间大约为11 小时

建议:4 hours/week...

英语(English)

字幕:英语(English)

您将学到的内容有

  • Check

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

  • Check

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

  • Check

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

  • Check

    Understand techniques to avoid overfitting and achieve good generalization performance.

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11 小时

建议:4 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 2 小时

Week 1: Diagnostics for Data

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
完成时间为 2 小时

Week 2: Codebases, Regularization, and Evaluating a Model

4 个视频 (总计 35 分钟), 4 个测验
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
完成时间为 1 小时

Week 3: Validation and Pipelines

4 个视频 (总计 24 分钟), 3 个测验
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
完成时间为 2 小时

Final Project

2 个阅读材料, 1 个测验
2 个阅读材料
Project Description10分钟
Where to Find Datasets10分钟

讲师

Avatar

Julian McAuley

Assistant Professor
Computer Science
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Ilkay Altintas

Chief Data Science Officer
San Diego Supercomputer Center

关于 加州大学圣地亚哥分校

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory....

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

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