課程信息
6,034 次近期查看

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為17 小時

建議:9 hours/week...

英語(English)

字幕:英語(English)

您將學到的內容有

  • 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% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為17 小時

建議:9 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 2 小時

Week 1: Diagnostics for Data

For this first week, we will go over the syllabus, download all course materials, and get your system up and running for the course. We will also introduce the basics of diagnostics for the results of supervised learning.

...
6 個視頻 (總計 49 分鐘), 4 個閱讀材料, 3 個測驗
6 個視頻
Motivation Behind the MSE8分鐘
Regression Diagnostics: MSE and R²6分鐘
Over- and Under-Fitting6分鐘
Classification Diagnostics: Accuracy and Error11分鐘
Classification Diagnostics: Precision and Recall12分鐘
4 個閱讀材料
Syllabus10分鐘
Setting Up Your System10分鐘
(Optional) Additional Resources and Recommended Readings10分鐘
Course Materials10分鐘
3 個練習
Review: Regression Diagnostics8分鐘
Review: Classification Diagnostics4分鐘
Diagnostics for Data30分鐘
2
完成時間為 2 小時

Week 2: Codebases, Regularization, and Evaluating a Model

This week, we will learn how to create a simple bag of words for analysis. We will also cover regularization and why it matters when building a model. Lastly, we will evaluate a model with regularization, focusing on classifiers.

...
4 個視頻 (總計 35 分鐘), 4 個測驗
4 個視頻
Model Complexity and Regularization10分鐘
Adding a Regularizer to our Model, and Evaluating the Regularized Model8分鐘
Evaluating Classifiers for Ranking4分鐘
4 個練習
Review: Setting Up a Codebase2分鐘
Review: Regularization5分鐘
Review: Evaluating a Model5分鐘
Codebases, Regularization, and Evaluating a Model45分鐘
3
完成時間為 1 小時

Week 3: Validation and Pipelines

This week, we will learn about validation and how to implement it in tandem with training and testing. We will also cover how to implement a regularization pipeline in Python and introduce a few guidelines for best practices.

...
4 個視頻 (總計 24 分鐘), 3 個測驗
4 個視頻
Validation5分鐘
“Theorems” About Training, Testing, and Validation8分鐘
Implementing a Regularization Pipeline in Python5分鐘
Guidelines on the Implementation of Predictive Pipelines5分鐘
3 個練習
Review: Validation4分鐘
Review: Predictive Pipelines6分鐘
Predictive Pipelines20分鐘
4
完成時間為 2 小時

Final Project

In the final week of this course, you will continue building on the project from the first and second courses of Python Data Products for Predictive Analytics with simple predictive machine learning algorithms. Find a dataset, clean it, and perform basic analyses on the data. Evaluate your model, validate your analyses, and make sure you aren't overfitting the data.

...
2 個閱讀材料, 1 個測驗
2 個閱讀材料
Project Description10分鐘
Where to Find Datasets10分鐘

講師

Avatar

Julian McAuley

Assistant Professor
Computer Science
Avatar

Ilkay Altintas

Chief Data Science Officer
San Diego Supercomputer Center

關於 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....

關於 Python Data Products for Predictive Analytics 專項課程

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

常見問題

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 您注册课程后,将有权访问专项课程中的所有课程,并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

還有其他問題嗎?請訪問 學生幫助中心