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Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Reading Material - PDF, article

Duration

6 hours, 25 minutes

Qualification

No formal qualification

Certificate

At completion

Additional info

Coming soon

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60 Hour TEYL Course Thumbnail

Overview

Excel-Powered Machine Learning: From Fundamentals to Predictive Analytics provides a thorough understanding of machine learning concepts, focusing on hands-on application through Microsoft Excel. Starting with an introduction to machine learning, students will learn key concepts and types of machine learning, setting the foundation for more advanced topics.

The course dives into building linear regression models, where you will explore the fundamentals of regression analysis, including understanding ordinary least squares (OLS) and interpreting regression outputs. Learners will work with Excel to prepare data, run regressions, and visualize relationships in their data.

In the advanced segment, you’ll learn how to perform multiple linear regression, exploring the assumptions behind OLS models, including linearity, normality, and multicollinearity. You’ll also understand how to use dummy variables in regression models and make data-driven predictions using Excel.

The course also introduces logistic regression, a key method for classification problems, and walks you through its applications in Excel. You'll explore model evaluation techniques like the ROC curve, learning how to interpret logistic regression coefficients and how to avoid issues like underfitting and overfitting.

By the end of the course, learners will have a strong grasp of how to leverage Excel for machine learning, providing practical, actionable insights into predictive analytics, and enhancing their ability to make data-driven decisions in business and research contexts.

This course is ideal for beginners and intermediate users who are looking to develop their machine learning skills using Microsoft Excel. It's especially beneficial for business analysts, data analysts, or anyone working with large datasets who wants to understand and apply machine learning techniques to improve decision-making. No prior experience in machine learning is required, but familiarity with Excel will be helpful.
To enroll in this course, you should have a basic understanding of Microsoft Excel, including the ability to work with spreadsheets and basic functions. No prior knowledge of machine learning or statistics is required, making this course suitable for individuals new to the field as well as those looking to refine their skills.

After completing this course, learners can pursue a variety of roles in data science, business analysis, and analytics, such as Data Analyst, Business Intelligence Analyst, and Marketing Analyst. The skills gained can also serve as a stepping stone for more advanced careers in machine learning, artificial intelligence, and predictive analytics. This course will help enhance career opportunities in industries that rely on data-driven decision-making, such as finance, marketing, healthcare, and technology.

Who is this course for?

Excel-Powered Machine Learning: From Fundamentals to Predictive Analytics provides a thorough understanding of machine learning concepts, focusing on hands-on application through Microsoft Excel. Starting with an introduction to machine learning, students will learn key concepts and types of machine learning, setting the foundation for more advanced topics.

The course dives into building linear regression models, where you will explore the fundamentals of regression analysis, including understanding ordinary least squares (OLS) and interpreting regression outputs. Learners will work with Excel to prepare data, run regressions, and visualize relationships in their data.

In the advanced segment, you’ll learn how to perform multiple linear regression, exploring the assumptions behind OLS models, including linearity, normality, and multicollinearity. You’ll also understand how to use dummy variables in regression models and make data-driven predictions using Excel.

The course also introduces logistic regression, a key method for classification problems, and walks you through its applications in Excel. You'll explore model evaluation techniques like the ROC curve, learning how to interpret logistic regression coefficients and how to avoid issues like underfitting and overfitting.

By the end of the course, learners will have a strong grasp of how to leverage Excel for machine learning, providing practical, actionable insights into predictive analytics, and enhancing their ability to make data-driven decisions in business and research contexts.

This course is ideal for beginners and intermediate users who are looking to develop their machine learning skills using Microsoft Excel. It's especially beneficial for business analysts, data analysts, or anyone working with large datasets who wants to understand and apply machine learning techniques to improve decision-making. No prior experience in machine learning is required, but familiarity with Excel will be helpful.
To enroll in this course, you should have a basic understanding of Microsoft Excel, including the ability to work with spreadsheets and basic functions. No prior knowledge of machine learning or statistics is required, making this course suitable for individuals new to the field as well as those looking to refine their skills.

After completing this course, learners can pursue a variety of roles in data science, business analysis, and analytics, such as Data Analyst, Business Intelligence Analyst, and Marketing Analyst. The skills gained can also serve as a stepping stone for more advanced careers in machine learning, artificial intelligence, and predictive analytics. This course will help enhance career opportunities in industries that rely on data-driven decision-making, such as finance, marketing, healthcare, and technology.

Requirements

Excel-Powered Machine Learning: From Fundamentals to Predictive Analytics provides a thorough understanding of machine learning concepts, focusing on hands-on application through Microsoft Excel. Starting with an introduction to machine learning, students will learn key concepts and types of machine learning, setting the foundation for more advanced topics.

The course dives into building linear regression models, where you will explore the fundamentals of regression analysis, including understanding ordinary least squares (OLS) and interpreting regression outputs. Learners will work with Excel to prepare data, run regressions, and visualize relationships in their data.

In the advanced segment, you’ll learn how to perform multiple linear regression, exploring the assumptions behind OLS models, including linearity, normality, and multicollinearity. You’ll also understand how to use dummy variables in regression models and make data-driven predictions using Excel.

The course also introduces logistic regression, a key method for classification problems, and walks you through its applications in Excel. You'll explore model evaluation techniques like the ROC curve, learning how to interpret logistic regression coefficients and how to avoid issues like underfitting and overfitting.

By the end of the course, learners will have a strong grasp of how to leverage Excel for machine learning, providing practical, actionable insights into predictive analytics, and enhancing their ability to make data-driven decisions in business and research contexts.

This course is ideal for beginners and intermediate users who are looking to develop their machine learning skills using Microsoft Excel. It's especially beneficial for business analysts, data analysts, or anyone working with large datasets who wants to understand and apply machine learning techniques to improve decision-making. No prior experience in machine learning is required, but familiarity with Excel will be helpful.
To enroll in this course, you should have a basic understanding of Microsoft Excel, including the ability to work with spreadsheets and basic functions. No prior knowledge of machine learning or statistics is required, making this course suitable for individuals new to the field as well as those looking to refine their skills.

After completing this course, learners can pursue a variety of roles in data science, business analysis, and analytics, such as Data Analyst, Business Intelligence Analyst, and Marketing Analyst. The skills gained can also serve as a stepping stone for more advanced careers in machine learning, artificial intelligence, and predictive analytics. This course will help enhance career opportunities in industries that rely on data-driven decision-making, such as finance, marketing, healthcare, and technology.

Career path

Excel-Powered Machine Learning: From Fundamentals to Predictive Analytics provides a thorough understanding of machine learning concepts, focusing on hands-on application through Microsoft Excel. Starting with an introduction to machine learning, students will learn key concepts and types of machine learning, setting the foundation for more advanced topics.

The course dives into building linear regression models, where you will explore the fundamentals of regression analysis, including understanding ordinary least squares (OLS) and interpreting regression outputs. Learners will work with Excel to prepare data, run regressions, and visualize relationships in their data.

In the advanced segment, you’ll learn how to perform multiple linear regression, exploring the assumptions behind OLS models, including linearity, normality, and multicollinearity. You’ll also understand how to use dummy variables in regression models and make data-driven predictions using Excel.

The course also introduces logistic regression, a key method for classification problems, and walks you through its applications in Excel. You'll explore model evaluation techniques like the ROC curve, learning how to interpret logistic regression coefficients and how to avoid issues like underfitting and overfitting.

By the end of the course, learners will have a strong grasp of how to leverage Excel for machine learning, providing practical, actionable insights into predictive analytics, and enhancing their ability to make data-driven decisions in business and research contexts.

This course is ideal for beginners and intermediate users who are looking to develop their machine learning skills using Microsoft Excel. It's especially beneficial for business analysts, data analysts, or anyone working with large datasets who wants to understand and apply machine learning techniques to improve decision-making. No prior experience in machine learning is required, but familiarity with Excel will be helpful.
To enroll in this course, you should have a basic understanding of Microsoft Excel, including the ability to work with spreadsheets and basic functions. No prior knowledge of machine learning or statistics is required, making this course suitable for individuals new to the field as well as those looking to refine their skills.

After completing this course, learners can pursue a variety of roles in data science, business analysis, and analytics, such as Data Analyst, Business Intelligence Analyst, and Marketing Analyst. The skills gained can also serve as a stepping stone for more advanced careers in machine learning, artificial intelligence, and predictive analytics. This course will help enhance career opportunities in industries that rely on data-driven decision-making, such as finance, marketing, healthcare, and technology.

    • Understanding Machine Learning Concepts 00:10:00
    • Key Types of Machine Learning Explained 00:10:00
    • Introduction to Linear Regression 00:10:00
    • Basics of Linear Regression in Practice 00:10:00
    • Visualising Linear Relationships in Excel 00:10:00
    • Excel Spreadsheet Preparation for Regression 00:10:00
    • Running Your First Linear Regression in Excel 00:10:00
    • Understanding Ordinary Least Squares (OLS) 00:10:00
    • Reading Regression Output – Part 1 00:10:00
    • Explaining Variability in Data 00:10:00
    • Reading Regression Output – Part 2 00:10:00
    • Reading Regression Output – Part 3 00:10:00
    • Exploring Multiple Regression Analysis 00:10:00
    • Applying Multiple Linear Regression in Excel 00:10:00
    • Analysing Output from Multiple Regression 00:10:00
    • Introduction to OLS Model Assumptions 00:10:00
    • OLS Assumption: Linearity 00:10:00
    • OLS Assumption: No Endogeneity 00:10:00
    • OLS Assumptions: Normality & Homoscedasticity 00:10:00
    • OLS Assumption: No Autocorrelation 00:10:00
    • OLS Assumption: No Multicollinearity 00:10:00
    • Using Dummy Variables in Regression 00:10:00
    • Making Data-Driven Predictions in Excel 00:10:00
    • What Is Logistic Regression? 00:10:00
    • Transitioning from Linear to Logistic Models 00:10:00
    • Logistic Function vs Logit Function 00:10:00
    • Performing Logistic Regression in Excel 00:10:00
    • How to Interpret Logistic Regression Coefficients 00:10:00
    • Case Study: Logistic Regression with XReal 00:10:00
    • Logistic Regression Output Explained – Part 2 00:10:00
    • Understanding ROC Curve and Model Evaluation 00:10:00
    • Dealing with Underfitting and Overfitting 00:10:00
    • Exam of Excel-Powered Machine Learning: From Fundamentals to Predictive Analytics 00:50:00
    • Premium Certificate 00:15:00
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Yes, our premium certificate and transcript are widely recognized and accepted by embassies worldwide, particularly by the UK embassy. This adds credibility to your qualification and enhances its value for professional and academic purposes.

Yes, this course is designed for learners of all levels, including beginners. The content is structured to provide step-by-step guidance, ensuring that even those with no prior experience can follow along and gain valuable knowledge.

Yes, professionals will also benefit from this course. It covers advanced concepts, practical applications, and industry insights that can help enhance existing skills and knowledge. Whether you are looking to refine your expertise or expand your qualifications, this course provides valuable learning.

No, you have lifetime access to the course. Once enrolled, you can revisit the materials at any time as long as the course remains available. Additionally, we regularly update our content to ensure it stays relevant and up to date.

I trust you’re in good health. Your free certificate can be located in the Achievement section. The option to purchase a CPD certificate is available but entirely optional, and you may choose to skip it. Please be aware that it’s crucial to click the “Complete” button to ensure the certificate is generated, as this process is entirely automated.

Yes, the course includes both assessments and assignments. Your final marks will be determined by a combination of 20% from assignments and 80% from assessments. These evaluations are designed to test your understanding and ensure you have grasped the key concepts effectively.

We are a recognized course provider with CPD, UKRLP, and AOHT membership. The logos of these accreditation bodies will be featured on your premium certificate and transcript, ensuring credibility and professional recognition.

Yes, you will receive a free digital certificate automatically once you complete the course. If you would like a premium CPD-accredited certificate, either in digital or physical format, you can upgrade for a small fee.

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Course Features

Price

Original price was: £490.00.Current price is: £14.99.

Study Method

Online | Self-paced

Course Format

Reading Material - PDF, article

Duration

6 hours, 25 minutes

Qualification

No formal qualification

Certificate

At completion

Additional info

Coming soon

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