About this Course

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Intermediate Level
Approx. 9 hours to complete
English

What you will learn

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

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

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

  • Understand techniques to avoid overfitting and achieve good generalization performance.

Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 9 hours to complete
English

Offered by

Placeholder

University of California San Diego

Syllabus - What you will learn from this course

Week
1

Week 1

3 hours to complete

Week 1: Diagnostics for Data

3 hours to complete
6 videos (Total 49 min), 4 readings, 3 quizzes
6 videos
Motivation Behind the MSE8m
Regression Diagnostics: MSE and R²6m
Over- and Under-Fitting6m
Classification Diagnostics: Accuracy and Error11m
Classification Diagnostics: Precision and Recall12m
4 readings
Syllabus10m
Setting Up Your System10m
(Optional) Additional Resources and Recommended Readings10m
Course Materials10m
3 practice exercises
Review: Regression Diagnostics30m
Review: Classification Diagnostics30m
Diagnostics for Data30m
Week
2

Week 2

2 hours to complete

Week 2: Codebases, Regularization, and Evaluating a Model

2 hours to complete
4 videos (Total 35 min)
4 videos
Model Complexity and Regularization10m
Adding a Regularizer to our Model, and Evaluating the Regularized Model8m
Evaluating Classifiers for Ranking4m
4 practice exercises
Review: Setting Up a Codebase30m
Review: Regularization5m
Review: Evaluating a Model5m
Codebases, Regularization, and Evaluating a Model45m
Week
3

Week 3

2 hours to complete

Week 3: Validation and Pipelines

2 hours to complete
4 videos (Total 24 min)
4 videos
“Theorems” About Training, Testing, and Validation8m
Implementing a Regularization Pipeline in Python5m
Guidelines on the Implementation of Predictive Pipelines5m
3 practice exercises
Review: Validation30m
Review: Predictive Pipelines30m
Predictive Pipelines20m
Week
4

Week 4

2 hours to complete

Final Project

2 hours to complete
2 readings
2 readings
Project Description10m
Where to Find Datasets10m

Reviews

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About the Python Data Products for Predictive Analytics Specialization

Python Data Products for Predictive Analytics

Frequently Asked Questions

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