Machine Learning in Excel

with Ivan Kitov
4.9/5
(536)

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

7 hours of content 7452 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 7 hours of content
  • 87 Interactive exercises
  • 63 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Machine Learning in Excel

A course by Ivan Kitov

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 7 hours of content
  • 87 Interactive exercises
  • 63 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 7 hours of content
  • 87 Interactive exercises
  • 63 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Acquire machine learning skills that bridge theoretical knowledge and practical application
  • Gain insight into the strength and limitations of various machine learning models
  • Leverage Excel’s beginner-friendly environment for hands-on machine learning modeling
  • Understand fundamental machine learning theory that will help you achieve outstanding real-world results
  • Combine Azure and Microsoft Excel to run ML experiments in the cloud
  • Improve your career prospects with in-demand machine learning skills, essential for your success in an AI-driven world

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

This course focuses on predictive modeling and enters multidimensional spaces that require an understanding of mathematical methods, transformations, and distributions. We will introduce you to all these concepts, gradually blending them into complex means of analysis such as k-means clustering and decision trees, while helping you hone your Excel skills.

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Course Introduction

1.1 Course Introduction

5 min

What Is Machine Learning?

1.2 What Is Machine Learning?

8 min

Types of Machine Learning

1.3 Types of Machine Learning

5 min

Linear Regression: Introduction

2.1 Linear Regression: Introduction

2 min

Linear Regression

2.3 Linear Regression

5 min

Linear Regression Model (Graphical Representation)

2.5 Linear Regression Model (Graphical Representation)

3 min

Curriculum

  • 1. Introduction
    3 Lessons 18 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 Introduction
    5 min
    What Is Machine Learning?
    8 min
    Types of Machine Learning
    5 min
  • 2. Simple Linear Regression
    10 Lessons 49 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.

    Linear Regression: Introduction
    2 min
    Linear Regression
    5 min
    Linear Regression Model (Graphical Representation)
    3 min
    Formatting Excel Spreadsheets
    1 min
    First Regression in Excel
    7 min
    What Is OLS?
    3 min
    Interpreting Regression Tables (Part 1)
    9 min
    Decomposition of Variability
    4 min
    Interpreting Regression Tables (Part 2)
    9 min
    Interpreting Regression Tables (Part 3)
    6 min
  • 3. Multiple Linear Regression
    13 Lessons 49 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.

    Multiple Regression Analysis
    3 min
    Multiple Linear Regression (Example)
    7 min
    Multiple Linear Regression (Results)
    7 min
    OLS Assumptions
    3 min
    OLS Assumptions: Linearity
    2 min
    OLS Assumptions: No Endogeneity
    4 min
    OLS Assumptions: Normality and Homoscedasticity
    5 min
    OSL Assumptions: No Autocorrelation
    4 min
    OLS Assumptions: No Multicollinearity
    4 min
    Dummy Variables
    5 min
    Dummy Variables - Exercise Read now
    1 min
    Making Predictions Using Linear Regression
    3 min
    Making Predictions Using Linear Regression -Exercise Read now
    1 min
  • 4. Linear Regression Practical Example
    6 Lessons 42 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.

    Practical Example (part 1)
    11 min
    Practical Example (part 2)
    6 min
    Practical Example (part 3)
    10 min
    A note on multicollinearity Read now
    1 min
    Feature Scaling
    3 min
    Practical Example (part 4)
    11 min
  • 5. Logistic Regression
    12 Lessons 70 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.

    Introduction to Logistic Regression
    2 min
    From Linear to Logistic Regression
    7 min
    Logistic vs. Logit Function
    4 min
    Applying Logistic Regression in Excel
    10 min
    Interpreting Regression Coefficients
    4 min
    Logistic Regression with Xreal
    5 min
    Understanding the Logistic Regression Summary (part 1)
    7 min
    Understanding the Logistic Regression Summary (Part 2)
    9 min
    ROC Curve
    5 min
    Binary Predictors for Logistic Regressions
    5 min
    Underfitting and Overfitting
    4 min
    Testing the Logistic Model
    8 min
  • 6. Cluster Analysis
    4 Lessons 15 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.

    Cluster Analysis (Definition)
    4 min
    Cluster Analysis (Application)
    5 min
    Clustering vs Classification
    3 min
    Cluster Analysis (Math Prerequisites)
    3 min
  • 7. K-means Clustering
    11 Lessons 58 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.

    K-means Clustering
    7 min
    K-means Clustering in Excel
    9 min
    K-means Clustering with Xreal
    4 min
    Choosing the Number of Clusters
    7 min
    Clustering Categorical Data
    2 min
    Standardization
    6 min
    Clustering and Regression
    2 min
    Clustering (Pros and Cons)
    4 min
    Types of Clustering
    4 min
    Market Segmentation (Part 1)
    7 min
    Market Segmentation (Part 2)
    6 min
  • 8. Decision Trees
    6 Lessons 28 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.

    Decision Trees
    3 min
    Entropy (Loss function)
    5 min
    Information Gain
    5 min
    Decision Trees in Excel (Part 1)
    7 min
    Decision Trees in Excel (part 2)
    4 min
    Decision trees (Prediction)
    4 min
  • 9. Machine Learning in the Cloud
    8 Lessons 30 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.

    Machine Learning in the Cloud
    4 min
    Setting up Azure Machine Learning Studio (AMLS)
    3 min
    First Experiment in AMLS (Part 1)
    8 min
    First Experiment in AMLS (Part 2)
    5 min
    Machine Learning in the Cloud Read now
    1 min
    Publishing a Web Service
    5 min
    Azure Assignment
    3 min
    The Future of Machine Learning Read now
    1 min

Topics

regression analysismachine learningk-means clusteringazure machine learning studiodata analysisTheoryExcellogistic regressiondecision trees

Tools & Technologies

excel

Course Requirements

  • Highly recommended to take the Intro to Excel, Statistics, Math, and Probability courses first
  • You will need Microsoft Office

Who Should Take This Course?

Level of difficulty: Intermediate

  • Aspiring data scientists and ML engineers
  • ML enthusiasts who want to learn in a beginner-friendly environment

Exams and Certification

A 365 Financial Analyst Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Ivan Kitov

Ivan Kitov

COO at

8 Courses

2894 Reviews

33406 Students

Ivan is the COO of 365 Data Science and a CFA charterholder with over 12 years of professional experience in the financial sector. He earned his Master’s degree in Financial Economics from the Erasmus University of Rotterdam, the Netherlands in 2010 and has been fascinated by the world of artificial intelligence and machine learning ever since. Seeing how data science truly redefined the finance industry over the last decade, Ivan knew that he couldn’t stay on the sidelines. In 2019, he published his first online course on corporate finance, combining his expertise with his love of teaching. His goal is to establish 365 Data Science as the best learning platform for aspiring data professionals in the world.

What Our Learners Say

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