Meaningful Predictive Modeling

University of California, San Diego via Coursera

Coursera based on 48 ratings

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Overview

The “Meaningful Predictive Modeling” course is designed for data scientists, analysts, and business professionals aiming to develop predictive models that deliver actionable and trustworthy insights. This course emphasizes not only the technical construction of predictive models but also their real-world relevance and impact. Participants will learn how to select appropriate modeling techniques based on business objectives and data characteristics. The course covers essential topics such as data preprocessing, feature engineering, model selection, evaluation metrics, and validation strategies to build robust...

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.

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.