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Meaningful Predictive Modeling
  • Level Professional
  • المدة 9 ساعات hours
  • الطبع بواسطة University of California San Diego
  • Offered by

عن

This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better? By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.

الوحدات

Course Introduction

    • 1 Videos
    • 3 Readings
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1 Videos

  • Introduction to Course 3: Meaningful Predictive Modeling

3 Readings

  • Syllabus
  • Setting Up Your System
  • (Optional) Additional Resources and Recommended Readings

Course Essentials

    • 1 Readings
    • 1 Discussion
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1 Discussions

  • What do you hope to get out of taking this course?

1 Readings

  • Course Materials

Regression Diagnostics

    • 3 Videos
    • 1 Assignment
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1 Assignment

  • Review: Regression Diagnostics

3 Videos

  • Motivation Behind the MSE
  • Regression Diagnostics: MSE and R²
  • Over- and Under-Fitting

Classification Diagnostics

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Review: Classification Diagnostics

2 Videos

  • Classification Diagnostics: Accuracy and Error
  • Classification Diagnostics: Precision and Recall

Week 1 Assessment

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Diagnostics for Data

1 Discussions

  • What is a resource or article about data you wish others knew about?

Setting Up a Codebase

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Review: Setting Up a Codebase

1 Videos

  • Setting Up a Codebase for Evaluation and Validation

Regularization and Evaluating a Model

    • 3 Videos
    • 2 Assignment
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2 Assignment

  • Review: Regularization
  • Review: Evaluating a Model

3 Videos

  • Model Complexity and Regularization
  • Adding a Regularizer to our Model, and Evaluating the Regularized Model
  • Evaluating Classifiers for Ranking

Week 2 Assessment

    • 1 Assignment
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1 Assignment

  • Codebases, Regularization, and Evaluating a Model

Validation

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Review: Validation

2 Videos

  • Validation
  • “Theorems” About Training, Testing, and Validation

Pipelines in Python

    • 2 Videos
    • 1 Assignment
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1 Assignment

  • Review: Predictive Pipelines

2 Videos

  • Implementing a Regularization Pipeline in Python
  • Guidelines on the Implementation of Predictive Pipelines

Week 3 Assessment

    • 1 Assignment
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1 Assignment

  • Predictive Pipelines

Project 3: Validating Predictions from Data

    • 2 Readings
    • 1 Discussion
    • 1 PeerReview
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1 Peer Review

  • Project Submission

1 Discussions

  • What is something you learned from doing this final project?

2 Readings

  • Project Description
  • Where to Find Datasets

Auto Summary

"Meaningful Predictive Modeling" focuses on evaluating and comparing regression and classification models within Big Data and Analytics. Taught by Coursera, this professional-level course spans 540 minutes and delves into diagnostic techniques, performance measures, and the training/validation/test pipeline to ensure model generalization to new data. Subscription options include Starter and Professional, targeting learners seeking advanced proficiency in predictive modeling.

Julian McAuley
Instructors

Julian McAuley

Ilkay Altintas
Instructors

Ilkay Altintas