Classification Models (Udacity)

Offered by Udacity, Alteryx,
Classification Models (Udacity)

Use data to predict categorical outcomes. The Classification Models course provides students with the foundational knowledge to use classification models to create business insights.

Class Deals by MOOC List - Click here and see Udacity's Active Discounts, Deals, and Promo Codes.

You will learn:

  • How classification modeling differs from modeling with numeric data
  • To use binary classification models to make predictions of binary outcomes
  • To use non-binary classification models to make predictions of non-binary outcomes

Throughout this course you’ll also learn the techniques to apply your knowledge in a data analytics program called Alteryx. At the end of the course, you’ll complete a project based on the principles in the course.
Predictive analytics has proved to be a powerful tool to help businesses analyze data and predict future outcomes and trends. In this course, you’ll learn how to use classification predictive models to solve business problems such as predicting whether or not a customer will respond to a marketing campaign, the likelihood of default on a loan, or which product a customer will buy. You'll learn this through improving your fluency in Alteryx, a data analytics tool that enables you prepare, blend, and analyze data quickly. This course is ideal for anyone who is interested in pursuing a career in business analysis, but lacks programming experience.
This course is part of the Business Analyst Nanodegree Program.

What You Will Learn

Lesson 1
Introduction to Classification Modeling

  • Learn the key terminology used in predictive modeling.
  • Learn the how to choose variables to use in a predictive model.
  • Practice preparing a dataset for modeling.

Lesson 2
Binary Classification Models

  • Learn how to use models to predict data with two possible outcomes.
  • Use logistic regression and decision tree models.
  • Learn how to compare models and interpret results.

Lesson 3
Non-Binary Classification Models

  • Learn how to use models to predict categorical data with three or more possible outcomes.
  • Learn how to use decision tree, forest, and boosted models.
  • Compare models and interpret results.

Prerequisites and Requirements

  • No programming experience required
  • Interested in using data to make better business decisions
  • Alteryx license (provided to nanodegree students at no cost,

compatible with Windows only)

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Statistical Learning (Coursera) Coursera
Illinois Tech

Statistical Learning (Coursera)

This course offers a deep dive into the world of statistical analysis, equipping learners with cutting-edge techniques to understand and interpret data effectively. We explore a range of methodologies, from regression and classification to advanced approaches like kernel methods and support vector machines, all designed to enhance your data analysis skills.

Nov 9th 2026
5-12 Weeks
Introduction to Machine Learning Course (Udacity) Udacity
Udacity

Introduction to Machine Learning Course (Udacity)

This class will teach you the end-to-end process of investigating data through a machine learning lens. Learn online, with Udacity. Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

Self Paced
Self-Paced
Business Statistics and Analysis Capstone (Coursera) Coursera
Rice University

Business Statistics and Analysis Capstone (Coursera)

The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available ‘Housing Data’ to pose various questions typically a client would pose to a data analyst. Your job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand.

Nov 9th 2026
4 Weeks
Data Analysis and Visualization (Udacity) Udacity
Georgia Institute of Technology,Udacity

Data Analysis and Visualization (Udacity)

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce students to the field by covering state­-of-­the-art modeling, analysis and visualization techniques. It will emphasize practical challenges involving complex real world data and include several case studies and hands-on work with the R programming language.

Self Paced
Self-Paced
Mastering Data Analysis in Excel (Coursera) Coursera
Duke University

Mastering Data Analysis in Excel (Coursera)

Important: The focus of this course is on math - specifically, data-analysis concepts and methods - not on Excel for its own sake. We use Excel to do our calculations, and all math formulas are given as Excel Spreadsheets, but we do not attempt to cover Excel Macros, Visual Basic, Pivot Tables, or other intermediate-to-advanced Excel functionality. This course will prepare you to design and implement realistic predictive models based on data. In the Final Project (module 6) you will assume the role of a business data analyst for a bank, and develop two different predictive models to determine which applicants for credit cards should be accepted and which rejected. Your first model will focus on minimizing default risk, and your second on maximizing bank profits.

Nov 2nd 2026
5-12 Weeks
Data Warehousing and Business Intelligence (Coursera) Coursera
University of California, Irvine

Data Warehousing and Business Intelligence (Coursera)

This course builds on “The Nature of Data and Relational Database Design” to extend the process of capturing and manipulating data through data warehousing and data mining. Once the transactional data is processed through ETL (Extract, Transform, Load), it is then stored in a data warehouse for use in managerial decision making. Data mining is one of the key enablers in the process of converting data stored in a data warehouse into actionable insight for better and faster decision making.

Nov 2nd 2026
4 Weeks
Introducción al Aprendizaje Profundo (Coursera) Coursera
Universidad Austral

Introducción al Aprendizaje Profundo (Coursera)

Este curso te brindará los conocimientos introductorios sobre Aprendizaje Profundo, vas a entender los fundamentos teóricos y su implementación . Se comenzará entendiendo cómo evolucionó el campo hasta llegar a las redes profundas y cuáles son sus principales beneficios frente a otras técnicas de aprendizaje supervisado, así como también sus limitaciones y situaciones en donde no posee un rendimiento superior

Nov 9th 2026
4 Weeks
Machine Learning Introduction for Everyone (Coursera) Coursera
IBM

Machine Learning Introduction for Everyone (Coursera)

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more.

Nov 9th 2026
3 Weeks
Introduction to Machine Learning in Sports Analytics (Coursera) Coursera
University of Michigan

Introduction to Machine Learning in Sports Analytics (Coursera)

In this course students will explore supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes. Building on the previous courses in the specialization, students will apply methods such as support vector machines (SVM), decision trees, random forest, linear and logistic regression, and ensembles of learners to examine data from professional sports leagues such as the NHL and MLB as well as wearable devices such as the Apple Watch and inertial measurement units (IMUs).

Nov 2nd 2026
4 Weeks
Machine Learning for Data Analysis (Coursera) Coursera
Wesleyan University

Machine Learning for Data Analysis (Coursera)

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Nov 2nd 2026
4 Weeks
Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Nov 16th 2026
5-12 Weeks
Create Machine Learning Models in Microsoft Azure (Coursera) Coursera
Microsoft

Create Machine Learning Models in Microsoft Azure (Coursera)

Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure.

Nov 2nd 2026
3 Weeks