Really grateful for the 3 day free courses. I have learnt more in 1 hour than my last 6 months of learning by attending other courses. It is to the point and explanations are great. Quizzes adds to our learning curve
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.
This course will help you master predictive modeling with multiple different approaches such as linear regression, logistic regression, cluster analysis, and decision trees in Excel. By the end of the course, you’ll understand the intuition behind machine learning algorithms and will be able to apply your knowledge in practice.
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 Free What Is Machine Learning? Free Types of Machine Learning Free
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 Free Linear Regression Free Linear Regression Model (Graphical Representation) Free Formatting Excel Spreadsheets Free First Regression in Excel Free What Is OLS? Free Interpreting Regression Tables (Part 1) Free Decomposition of Variability Free Interpreting Regression Tables (Part 2) Interpreting Regression Tables (Part 3)
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 Multiple Linear Regression (Example) Multiple Linear Regression (Results) OLS Assumptions OLS Assumptions: Linearity OLS Assumptions: No Endogeneity OLS Assumptions: Normality and Homoscedasticity OSL Assumptions: No Autocorrelation OLS Assumptions: No Multicollinearity Dummy Variables Making Predictions Using Linear Regression
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) Practical Example (part 2) Practical Example (part 3) A note on multicollinearity Feature Scaling Practical Example (part 4)
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 From Linear to Logistic Regression Logistic vs. Logit Function Applying Logistic Regression in Excel Interpreting Regression Coefficients Logistic Regression with Xreal Understanding the Logistic Regression Summary (part 1) Understanding the Logistic Regression Summary (Part 2) ROC Curve Binary Predictors for Logistic Regressions Underfitting and Overfitting Testing the Logistic 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.Cluster Analysis (Definition) Cluster Analysis (Application) Clustering vs Classification Cluster Analysis (Math Prerequisites)
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 K-means Clustering in Excel K-means Clustering with Xreal Choosing the Number of Clusters Clustering Categorical Data Standardization Clustering and Regression Clustering (Pros and Cons) Types of Clustering Market Segmentation (Part 1) Market Segmentation (Part 2)
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 Entropy (Loss function) Information Gain Decision Trees in Excel (Part 1) Decision Trees in Excel (part 2) Decision trees (Prediction)
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 Setting up Azure Machine Learning Studio (AMLS) First Experiment in AMLS (Part 1) First Experiment in AMLS (Part 2) Publishing a Web Service Azure Assignment The Future of Machine Learning
with Ivan Kitov