Machine Learning Algorithms with Python in Business Analytics

Machine Learning Algorithms with Python in Business Analytics

This course is part of Business Analytics Specialization

Instructors: Ronald Guymon

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8,881 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

41 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

41 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Implement a conceptual framework of machine learning algorithms for finding actionable insight to business problems.
Apply a conceptual foundation for interpreting machine learning results from regression, classification, and clustering algorithms.
Apply machine learning algorithms to business data.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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This course is part of the Business 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

One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use Python to run these algorithms and communicate the results.

In this module, we will examine why exploratory data analysis alone may not yield actionable business insights, evaluate the key steps of the machine learning workflow, and apply preprocessing techniques using scikit-learn to prepare data for analysis with various algorithms.

What's included

18 videos8 readings2 assignments1 discussion prompt1 plugin

18 videos Total 120 minutes

Course Introduction 4 minutes
About Professor Kim Mendoza 2 minutes
About Professor Ron Guymon 3 minutes
The Impact of the Gies Community 2 minutes
Module 1 Introduction 3 minutes
The Need for Algorithms and Data Models 14 minutes
Introduction to Machine Learning 7 minutes
Machine Learning and Statistics in Business 8 minutes
Introduction to the Machine-Learning Workflow 5 minutes
Introduction to the Course Data Set, TECA 5 minutes
Choose the Algorithm 9 minutes
General Processing Tasks 13 minutes
Preprocessing Categorical Variables 13 minutes
Preprocessing Numeric Variables 6 minutes
Calculate Results: Classification Workflow Example 9 minutes
Calculate Results: Regression Workflow Example 8 minutes
Calculate Results: Clustering Workflow Example 8 minutes
Module 1 Conclusion 2 minutes

8 readings Total 80 minutes

Syllabus 10 minutes
Coursera Coach 10 minutes
About the Discussion Forums 10 minutes
Online Education at Gies College of Business 10 minutes
Updating Your Profile 10 minutes
Module 1 Overview 10 minutes
Module 1 Files 10 minutes
Module 1 Study Guide 10 minutes

2 assignments Total 60 minutes

Orientation Quiz 30 minutes
Introduction to Machine Learning (ML): Quiz 30 minutes

1 discussion prompt Total 10 minutes

Getting to Know Your Classmates 10 minutes

1 plugin Total 15 minutes

Welcome! Please Tell Us About Yourself 15 minutes

In this module, we will learn how regression can be used for prediction, relationship exploration, and inference in business contexts by applying scikit-learn and scipy, and will evaluate the effectiveness of regression models across different applications.

What's included

12 videos3 readings1 assignment1 peer review

12 videos Total 131 minutes

Module 2 Introduction 6 minutes
Business Problem 4 minutes
Data 5 minutes
What Problems Can Regression Answer? 9 minutes
Linear Models 10 minutes
Simple Linear Regression 23 minutes
Multiple Regression 16 minutes
Prediction with Linear Regression 14 minutes
Non-Linear Models 6 minutes
Decision Tree Regression Intuition 15 minutes
Decision Tree Regression Application 21 minutes
Module 2 Conclusion 3 minutes

3 readings Total 30 minutes

Module 2 Overview 10 minutes
Module 2 Files 10 minutes
Module 2 Study Guide 10 minutes

1 assignment Total 30 minutes

Regression Algorithms: Quiz 30 minutes

1 peer review Total 180 minutes

Regression Algorithms: Peer Reviewed Assignment 180 minutes

In this module, we will deepen our understanding of how classification algorithms are applied in business by running and interpreting K-nearest neighbors and decision tree models.

What's included

12 videos3 readings1 assignment

12 videos Total 107 minutes

Module 3 Introduction 3 minutes
Classification Algorithms 6 minutes
Business Problem 5 minutes
Data 5 minutes
Logistic Regression Intuition 8 minutes
Logistic Regression Application 21 minutes
Confusion Matrix 11 minutes
Decision Tree Classification Intuition 5 minutes
Decision Tree Application 17 minutes
KNN Intuition 8 minutes
KNN Application 15 minutes
Module 3 Conclusion 3 minutes

3 readings Total 30 minutes

Module 3 Overview 10 minutes
Module 3 Files 10 minutes
Module 3 Study Guide 10 minutes

1 assignment Total 30 minutes

Classification Algorithms: Quiz 30 minutes

In this module, we will gain a deeper understanding of how clustering algorithms are used in business by running and interpreting K-means and DBSCAN models.

What's included

10 videos5 readings1 assignment1 discussion prompt1 plugin

10 videos Total 85 minutes

Module 4 Introduction 4 minutes
Introduction to Clustering 12 minutes
Business Problem 4 minutes
Data 3 minutes
K-Means Intuition 12 minutes
K-Means Application 19 minutes
DBSCAN Intuition 9 minutes
DBSCAN Application 16 minutes
Module 4 Conclusion 5 minutes
Learn on Your Terms 1 minute

5 readings Total 50 minutes

Module 4 Overview 10 minutes
Module 4 Files 10 minutes
Module 4 Study Guide 10 minutes
Congratulations on Completing the Course! 10 minutes
Get Your Course Certificate 10 minutes

1 assignment Total 30 minutes

Clustering Algorithms: Quiz 30 minutes

1 discussion prompt Total 10 minutes

Refelection 10 minutes

1 plugin Total 15 minutes

How Was the Course? 15 minutes

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Build toward a degree

This course is part of the following degree program(s) offered by University of Illinois Urbana-Champaign. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹

Instructors

Instructor ratings

(11 ratings)

Ronald Guymon

University of Illinois Urbana-Champaign

5 Courses 90,531 learners

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University of Illinois Urbana-Champaign

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Reviewed on Aug 31, 2022

A course to learn some notions to apply data science in business. I would have liked more examples, but still, it is a good starting point to specialise in this knowledge.

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