課程信息
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立即開始,按照自己的計劃學習。

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中級

完成時間大約為11 小時

建議:4 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.

學習Course的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Data Engineers
  • Data Analysts
  • Consultants

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11 小時

建議:4 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

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

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

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

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

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Julian McAuley

Assistant Professor
Computer Science
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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

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