Meaningful Predictive Modeling (Coursera)

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.

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.

Course 3 of 4 in the Python Data Products for Predictive Analytics Specialization.

Syllabus

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.

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.

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.

WEEK 4

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.