Online Course top-rated
Machine Learning in Excel

Name: Machine Learning in Excel Course
Price: 36 USD

with Ivan Kitov

Master the core concepts of popular ML algorithms with hands-on projects in Excel’s beginner-friendly environment

4.8

862 reviews on

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9,453 students already enrolled

Skill level:

Intermediate

Duration:

7 hours

Lessons (6 hours)

Practice exams (1.33 hours)

Projects (4 hours)

CPE credits:

9.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Acquire ML skills in a beginner-friendly environment.
Understand the strengths and limitations of ML models.
Use Excel for hands-on machine learning modeling.
Combine Azure and Excel to run ML experiments in the cloud.
Improve your career prospects with in-demand machine learning skills.

Topics & tools

Regression AnalysisMachine LearningK-Means ClusteringAzure Machine Learning StudioData AnalysisTheoryExcelLogistic RegressionDecision TreesMachine and Deep LearningSpreadsheets

Your instructor

Ivan Kitov

Chief Operating Officer at 365 Data Science | Finance Professional | Educator in Corporate Finance, Investments & Data

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Financial Economics

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Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 9.5 Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study

Machine learning is one of the most in-demand skills in business today—and you don’t need to be a programmer to get started. With the right tools, you can apply powerful machine learning techniques directly in Excel to solve real-world problems.

In this Machine Learning in Excel course, you’ll learn how to build and interpret machine learning models using familiar spreadsheet functions and the Real Statistics add-in—no coding required. This hands-on course is perfect for analysts, business professionals, and students looking to gain practical machine learning experience without learning Python or R.

We begin by introducing you to the key types of machine learning: supervised and unsupervised learning. You’ll explore foundational models like linear and logistic regression, which allow you to make predictions and classify data based on historical patterns. Then, we’ll shift focus to clustering techniques, such as k-means and hierarchical clustering, used for customer segmentation and pattern recognition.

Next, you’ll learn how decision trees work by using concepts like entropy and information gain. You’ll build trees step-by-step in Excel and learn how to evaluate their predictive performance. Each technique is broken down clearly and reinforced with visual aids, Excel walkthroughs, and relatable examples.

The final section of the course introduces you to Microsoft Azure Machine Learning Studio (AMLS)—a cloud-based tool for building machine learning models using a drag-and-drop interface. You’ll build your first predictive model, deploy it as a web service, and connect it to Excel for real-time predictions.

Machine Learning in Excel is a must-have course for anyone who wants to leverage data-driven insights using tools they already know. Whether you're looking to improve decision-making, analyze customer behavior, or explore data science, this course will give you the confidence to apply machine learning in your day-to-day work. Enroll today and unlock the power of machine learning—right inside Excel.

Prerequisites

Microsoft Excel (any recent version, such as Excel 2019, 2021, or Microsoft 365)
Intermediate Excel skills are required.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

82 lessons 48 exercises 1 project 5 exams

1. Introduction

26 min

In this introductory section, we will discuss why you need to learn advanced statistics, what sets this discipline apart from machine learning, and how you can get the most out of the Machine Learning in Excel course.

26 min

In this introductory section, we will discuss why you need to learn advanced statistics, what sets this discipline apart from machine learning, and how you can get the most out of the Machine Learning in Excel course.

Course Guide and Syllabus Free

Course Introduction Free

What Is Machine Learning? Free

Types of Machine Learning Free

Exercise Free
2. Simple Linear Regression

50 min

Join us to create your first simple regression in Excel and get familiar with a very important statistical concept – the Ordinary least squares framework. You will learn about OLS assumptions, how to interpret regression results, as well as how to decompose variability.

50 min

Join us to create your first simple regression in Excel and get familiar with a very important statistical concept – the Ordinary least squares framework. You will learn about OLS assumptions, how to interpret regression results, as well as how to decompose variability.

Section 2: Learning Objectives

Linear Regression: Introduction Free

Linear Regression Free

Exercise

Linear Regression Model (Graphical Representation) Free

Exercise

Formatting Excel Spreadsheets Free

First Regression in Excel Free

What Is OLS? Free

Interpreting Regression Tables (Part 1) Free

Exercise

Decomposition of Variability Free

Interpreting Regression Tables (Part 2)

Interpreting Regression Tables (Part 3)

Exercise
3. Multiple Linear Regression

50 min

In section 3 you will discover multiple linear regression. We will expand on the simple linear regression techniques we covered in the previous section and discuss some practical considerations such as working with dummy variables and how to make predictions with more than one independent variable using Excel.

50 min

In section 3 you will discover multiple linear regression. We will expand on the simple linear regression techniques we covered in the previous section and discuss some practical considerations such as working with dummy variables and how to make predictions with more than one independent variable using Excel.

Section 3: Learning Objectives

Multiple Regression Analysis

Multiple Linear Regression (Example)

Multiple Linear Regression (Results)

Exercise

OLS Assumptions

OLS Assumptions: Linearity

OLS Assumptions: No Endogeneity

OLS Assumptions: Normality and Homoscedasticity

OSL Assumptions: No Autocorrelation

OLS Assumptions: No Multicollinearity

Exercise

Dummy Variables

Dummy Variables - Exercise

Making Predictions Using Linear Regression

Making Predictions Using Linear Regression -Exercise
4. Linear Regression Practical Example

43 min

An all-in-one use case that tests your understanding of each of the concepts you mastered so far. We will focus on a property price dataset and create a linear regression model to predict house prices.

43 min

An all-in-one use case that tests your understanding of each of the concepts you mastered so far. We will focus on a property price dataset and create a linear regression model to predict house prices.

Section 4: Learning Objectives

Practical Example (part 1)

Practical Example (part 2)

Practical Example (part 3)

A note on multicollinearity

Feature Scaling

Practical Example (part 4)

Practice exam
5. Logistic Regression

71 min

This section of the course covers logistic regression. You will grasp the difference between logistic and logit regression, the concepts of ROC curve, underfitting and overfitting, and how to interpret results from a logistic regression. Of course, you will see a practical example of how to perform this type of regression in Excel and calculate the accuracy of your model.

71 min

This section of the course covers logistic regression. You will grasp the difference between logistic and logit regression, the concepts of ROC curve, underfitting and overfitting, and how to interpret results from a logistic regression. Of course, you will see a practical example of how to perform this type of regression in Excel and calculate the accuracy of your model.

Section 5: Learning Objectives

Introduction to Logistic Regression

From Linear to Logistic Regression

Logistic vs. Logit Function

Applying Logistic Regression in Excel

Interpreting Regression Coefficients

Logistic Regression with Xreal

Exercise

Understanding the Logistic Regression Summary (part 1)

Understanding the Logistic Regression Summary (Part 2)

Exercise

ROC Curve

Binary Predictors for Logistic Regressions

Underfitting and Overfitting

Testing the Logistic Model

Exercise

Practice exam
6. Cluster Analysis

16 min

Cluster analysis is the most intuitive and important example of unsupervised learning. However, to be able to understand cluster analysis, you must first become familiar with the mathematics behind it. Here we will explore the fundamentals of cluster analysis and have a look at the differences between clustering and classification.

16 min

Cluster analysis is the most intuitive and important example of unsupervised learning. However, to be able to understand cluster analysis, you must first become familiar with the mathematics behind it. Here we will explore the fundamentals of cluster analysis and have a look at the differences between clustering and classification.

Section 6: Learning Objectives

Cluster Analysis (Definition)

Cluster Analysis (Application)

Clustering vs Classification

Cluster Analysis (Math Prerequisites)

Exercise
7. K-means Clustering

59 min

Master K-means clustering in Excel by learning how to choose the number of clusters in your analysis and determine when to standardize or not standardize your data. At the end of this section, we will go through a complete practical example that includes marketing segmentation with cluster analysis.

59 min

Master K-means clustering in Excel by learning how to choose the number of clusters in your analysis and determine when to standardize or not standardize your data. At the end of this section, we will go through a complete practical example that includes marketing segmentation with cluster analysis.

Section 7: Learning Objectives

K-means Clustering

K-means Clustering in Excel

K-means Clustering with Xreal

Choosing the Number of Clusters

Clustering Categorical Data

Exercise

Standardization

Clustering and Regression

Clustering (Pros and Cons)

Types of Clustering

Exercise

Market Segmentation (Part 1)

Market Segmentation (Part 2)

Practice exam
8. Decision Trees

29 min

With the use of visual examples this section of the course introduces you to the concept of decision trees. We will cover the advantages and disadvantages of this method and explore its inner workings – how is the tree constructed and what metrics are used in its construction. This will be followed up by a practical example showcasing how to create decision trees in Excel.

29 min

With the use of visual examples this section of the course introduces you to the concept of decision trees. We will cover the advantages and disadvantages of this method and explore its inner workings – how is the tree constructed and what metrics are used in its construction. This will be followed up by a practical example showcasing how to create decision trees in Excel.

Section 8: Learning Objectives

Decision Trees

Entropy (Loss function)

Information Gain

Decision Trees in Excel (Part 1)

Decision Trees in Excel (part 2)

Decision trees (Prediction)

Exercise

Practice exam
9. Machine Learning in the Cloud

31 min

In the final section of the course, we will combine Azure and Microsoft Excel to run ML experiments in the cloud. In our case, we’ll create a predictive analytics model in Azure Machine Learning Studio.

31 min

In the final section of the course, we will combine Azure and Microsoft Excel to run ML experiments in the cloud. In our case, we’ll create a predictive analytics model in Azure Machine Learning Studio.

Section 9: Learning Objectives

Machine Learning in the Cloud

Exercise

Setting up Azure Machine Learning Studio (AMLS)

First Experiment in AMLS (Part 1)

First Experiment in AMLS (Part 2)

Machine Learning in the Cloud (Assignment)

Publishing a Web Service

Exercise

Azure Assignment

The Future of Machine Learning
10. Course project and exam

340 min

340 min

Regression Analysis in Excel Project

Course exam

Free lessons

1.2 Course Introduction

5 min

1.3 What Is Machine Learning?

8 min

1.4 Types of Machine Learning

5 min

2.2 Linear Regression: Introduction

2 min

2.3 Linear Regression

5 min

2.5 Linear Regression Model (Graphical Representation)

3 min

Start for free

The experience was great, completely a beginner friendly course which helps you to lay a foundation for the upcoming advanced ai courses. Thank you for providing precise lectures.

Pragnya R.

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9 in 10

people walk away career-ready

with practical data and AI skills.

This course is highly recommended, and the Tutor did a great job as all topics were excellently delivered. I totally enjoyed it notwithstanding being a newbie.

Adebukola C.

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96%

of our students recommend

365 Data Science.

The 365 Data Science program helped me transition to a new role where I apply Python, Excel, and Power BI daily. I analyzed mystery shopper data at work using skills from the courses, built stunning visuals, and presented insights to my team. The practical examples showed me how things work in real life—not just in theory. I’ve recommended 365 to friends and colleagues because it genuinely prepares you for real-world data tasks.