Pragnya R.
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Online Course
top-rated
Machine Learning in Excel
with
Ivan Kitov
Master the core concepts of popular ML algorithms with hands-on projects in Excel’s beginner-friendly environment
9226 students already have enrolled
Skill level:
Intermediate
Duration:
7 hours
-
Lessons (6 hours)
-
Practice exams (1.33 hours)
-
Projects (4 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
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
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
Course OVERVIEW
Description
CPE Credits: 9.5
Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study
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
- 2. Simple Linear Regression50 MinJoin 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 ObjectivesLinear Regression: Introduction FreeLinear Regression FreeExerciseLinear Regression Model (Graphical Representation) FreeExerciseFormatting Excel Spreadsheets FreeFirst Regression in Excel FreeWhat Is OLS? FreeInterpreting Regression Tables (Part 1) FreeExerciseDecomposition of Variability FreeInterpreting Regression Tables (Part 2)Interpreting Regression Tables (Part 3)Exercise
- 3. Multiple Linear Regression50 MinIn 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 ObjectivesMultiple Regression AnalysisMultiple Linear Regression (Example)Multiple Linear Regression (Results)ExerciseOLS AssumptionsOLS Assumptions: LinearityOLS Assumptions: No EndogeneityOLS Assumptions: Normality and HomoscedasticityOSL Assumptions: No AutocorrelationOLS Assumptions: No MulticollinearityExerciseDummy VariablesDummy Variables - ExerciseMaking Predictions Using Linear RegressionMaking Predictions Using Linear Regression -Exercise
- 4. Linear Regression Practical Example43 MinAn 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.
- 5. Logistic Regression71 MinThis 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 ObjectivesIntroduction to Logistic RegressionFrom Linear to Logistic RegressionLogistic vs. Logit FunctionApplying Logistic Regression in ExcelInterpreting Regression CoefficientsLogistic Regression with XrealExerciseUnderstanding the Logistic Regression Summary (part 1)Understanding the Logistic Regression Summary (Part 2)ExerciseROC CurveBinary Predictors for Logistic RegressionsUnderfitting and OverfittingTesting the Logistic ModelExercisePractice exam
- 6. Cluster Analysis16 MinCluster 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.
- 7. K-means Clustering59 MinMaster 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 ObjectivesK-means ClusteringK-means Clustering in ExcelK-means Clustering with XrealChoosing the Number of ClustersClustering Categorical DataExerciseStandardizationClustering and RegressionClustering (Pros and Cons)Types of ClusteringExerciseMarket Segmentation (Part 1)Market Segmentation (Part 2)Practice exam
- 8. Decision Trees29 MinWith 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.
- 9. Machine Learning in the Cloud31 MinIn 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 ObjectivesMachine Learning in the CloudExerciseSetting 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 ServiceExerciseAzure AssignmentThe Future of Machine Learning
- 10. Course project and exam340 Min
Free preview
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
9 in 10
people walk away career-ready
with practical data and AI skills.
96%
of our students recommend
365 Data Science.
#1 most reviewed
AI and data learning platform on Trustpilot.
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
Certificates are included with the Self-Study learning plan.
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