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.

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.

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.

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.

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.

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.

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.

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.

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.