Meaningful Predictive Modeling

Meaningful Predictive Modeling

This course is part of Python Data Products for Predictive Analytics Specialization

Instructors: Julian McAuley

Access provided by Emerson Electric

6,617 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

49 reviews

Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

49 reviews

Intermediate level

Some related experience required

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll 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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

10 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Python Data Products for Predictive Analytics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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?

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.

What's included

6 videos4 readings3 assignments2 discussion prompts

6 videos Total 49 minutes

Introduction to Course 3: Meaningful Predictive Modeling 4 minutes
Motivation Behind the MSE 9 minutes
Regression Diagnostics: MSE and R² 7 minutes
Over- and Under-Fitting 7 minutes
Classification Diagnostics: Accuracy and Error 11 minutes
Classification Diagnostics: Precision and Recall 12 minutes

4 readings Total 40 minutes

Syllabus 10 minutes
Setting Up Your System 10 minutes
(Optional) Additional Resources and Recommended Readings 10 minutes
Course Materials 10 minutes

3 assignments Total 90 minutes

Diagnostics for Data 30 minutes
Review: Regression Diagnostics 30 minutes
Review: Classification Diagnostics 30 minutes

2 discussion prompts Total 20 minutes

What do you hope to get out of taking this course? 10 minutes
What is a resource or article about data you wish others knew about? 10 minutes

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.

What's included

4 videos4 assignments

4 videos Total 35 minutes

Setting Up a Codebase for Evaluation and Validation 12 minutes
Model Complexity and Regularization 10 minutes
Adding a Regularizer to our Model, and Evaluating the Regularized Model 8 minutes
Evaluating Classifiers for Ranking 4 minutes

4 assignments Total 85 minutes

Codebases, Regularization, and Evaluating a Model 45 minutes
Review: Setting Up a Codebase 30 minutes
Review: Regularization 5 minutes
Review: Evaluating a Model 5 minutes

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.

What's included

4 videos3 assignments

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.