Meaningful Predictive Modeling

Meaningful Predictive Modeling

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

Instructors: Julian McAuley

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

Machine Learning

Details to know

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Assessments

10 assignments

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.

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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 videosTotal 49 minutes

Introduction to Course 3: Meaningful Predictive Modeling3 minutesPreview module
Motivation Behind the MSE8 minutes
Regression Diagnostics: MSE and R²6 minutes
Over- and Under-Fitting6 minutes
Classification Diagnostics: Accuracy and Error11 minutes
Classification Diagnostics: Precision and Recall12 minutes

4 readingsTotal 40 minutes

Syllabus10 minutes
Setting Up Your System10 minutes
(Optional) Additional Resources and Recommended Readings10 minutes
Course Materials10 minutes

3 assignmentsTotal 90 minutes

Review: Regression Diagnostics30 minutes
Review: Classification Diagnostics30 minutes
Diagnostics for Data30 minutes

2 discussion promptsTotal 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 videosTotal 35 minutes

Setting Up a Codebase for Evaluation and Validation12 minutesPreview module
Model Complexity and Regularization10 minutes
Adding a Regularizer to our Model, and Evaluating the Regularized Model8 minutes
Evaluating Classifiers for Ranking4 minutes

4 assignmentsTotal 85 minutes

Review: Setting Up a Codebase30 minutes
Review: Regularization5 minutes
Review: Evaluating a Model5 minutes
Codebases, Regularization, and Evaluating a Model45 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.