このコースについて
5,236 最近の表示

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約17時間で修了

推奨:9 hours/week...

英語

字幕:英語

学習内容

  • 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%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

中級レベル

約17時間で修了

推奨:9 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
2時間で修了

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件のビデオ (合計49分), 4 readings, 3 quizzes
6件のビデオ
Motivation Behind the MSE8 分
Regression Diagnostics: MSE and R²6 分
Over- and Under-Fitting6 分
Classification Diagnostics: Accuracy and Error11 分
Classification Diagnostics: Precision and Recall12 分
4件の学習用教材
Syllabus10 分
Setting Up Your System10 分
(Optional) Additional Resources and Recommended Readings10 分
Course Materials10 分
3の練習問題
Review: Regression Diagnostics8 分
Review: Classification Diagnostics4 分
Diagnostics for Data30 分
2
2時間で修了

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件のビデオ (合計35分), 4 quizzes
4件のビデオ
Model Complexity and Regularization10 分
Adding a Regularizer to our Model, and Evaluating the Regularized Model8 分
Evaluating Classifiers for Ranking4 分
4の練習問題
Review: Setting Up a Codebase2 分
Review: Regularization5 分
Review: Evaluating a Model5 分
Codebases, Regularization, and Evaluating a Model45 分
3
1時間で修了

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件のビデオ (合計24分), 3 quizzes
4件のビデオ
“Theorems” About Training, Testing, and Validation8 分
Implementing a Regularization Pipeline in Python5 分
Guidelines on the Implementation of Predictive Pipelines5 分
3の練習問題
Review: Validation4 分
Review: Predictive Pipelines6 分
Predictive Pipelines20 分
4
2時間で修了

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 readings, 1 quiz
2件の学習用教材
Project Description10 分
Where to Find Datasets10 分

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

カリフォルニア大学サンディエゴ校について

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....

Python Data Products for Predictive Analyticsの専門講座について

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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