Supervised Text Classification for Marketing Analytics

University of Colorado Boulder via Coursera

Go to Course: https://www.coursera.org/learn/supervised-text-classification-for-marketing-analytics

Introduction

Describe text classification and related terminology (e.g., supervised machine learning)

Apply text classification to marketing data through a peer-graded project

Apply text classification to a variety of popular marketing use cases via structured homeworks

Train, evaluate and improve the performance of the text classification models you create for your final project

Syllabus

The Supervised Machine Learning Workflow

In this module, we will learn about the different types of machine learning that exist and the operational steps of building a supervised machine learning model. We will also cover performance metrics of text classification.

Neural Networks and Deep Learning

In this module, we will learn about neural networks and supervised machine learning. Then we will dive into real supervised machine learning projects and the key decisions that need to be made when conducting one's own project.

Getting Started with Google Colab and Deep Learning

In this module, we will learn how to work in the Google Colab and Google Drive environment. We will get started with supervised learning by using a wrapper for Google’s Tensorflow and transformer models.

Linear Models and Classification Metrics

In this module, we will learn how to workshop a variety of supervised machine learning models that rely on linear-based models. We will also learn how to perform an external performance analysis of models in sci-kit learn.