Unsupervised learning methods are central to your journey in data science. Learn how to reduce the number of dimensions in your data set, aggregate data into factors, and cluster your data points to make clear, robust conclusions about your data!

Reduce the number of dimensions, aggregate data into factors, and cluster your data points to make clear, robust conclusions!

with R

Unsupervised Machine Learning

The k-means algorithm is one common approach to clustering. Learn how the algorithm works under the hood, implement k-means clustering in R, visualize and interpret the results, and select the number of clusters when it's not known ahead of time. By the end of the chapter, you'll have applied k-means clustering to a fun "real-world" dataset!

Unsupervised learning in R

Hierarchical clustering is another popular method for clustering. The goal of this chapter is to go over how it works, how to use it, and how it compares to k-means clustering.

Hierarchical clustering

Principal component analysis, or PCA, is a common approach to dimensionality reduction. Learn exactly what PCA does, visualize the results of PCA with biplots and scree plots, and deal with practical issues such as centering and scaling the data before performing PCA.

Dimensionality reduction with PCA

The goal of this chapter is to guide you through a complete analysis using the unsupervised learning techniques covered in the first three chapters. You'll extend what you've learned by combining PCA as a preprocessing step to clustering using data that consist of measurements of cell nuclei of human breast masses.

Putting it all together with a case study

Pokemon data

Wisconsin breast cancer data

Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective, so that you can get from data to insights as quickly as possible.


Introduction to R

This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective to get insights quickly.

Unsupervised Learning in R

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Machine Learning

Cluster analysis seeks to find groups of observations that are similar to one another, but the identified groups are different from each other. This similarity/difference is captured by the metric called distance. In this chapter, you will learn how to calculate the distance between observations for both continuous and categorical features. You will also develop an intuition for how the scales of your features can affect distance.

Calculating distance between observations

This chapter will help you answer the last question from chapter 1 - how do you find groups of similar observations (clusters) in your data using the distances that you have calculated? You will learn about the fundamental principles of hierarchical clustering - the linkage criteria and the dendrogram plot - and how both are used to build clusters. You will also explore data from a  wholesale distributor in order to perform market segmentation of clients using their spending habits.

In this chapter, you will build an understanding of the principles behind the k-means algorithm,  learn how to select the right k when it isn't previously known, and revisit the wholesale data from a different perspective.  

K-means clustering

In this chapter, you will apply the skills you have learned to explore how the average salary amongst professions have changed over time.  

Case Study: National Occupational mean wage

Soccer player positions

Occupational Employment Statistics (OES)

Wholesale customer spending

Cluster analysis is a powerful toolkit in the data science workbench. It is used to find groups of observations (clusters) that share similar characteristics. These similarities can inform all kinds of business decisions; for example, in marketing, it is used to identify distinct groups of customers for which advertisements can be tailored. In this course, you will learn about two commonly used clustering methods -  hierarchical clustering and k-means clustering. You won't just learn how to use these methods, you'll build a <strong> strong intuition</strong> for how they work and how to interpret their results. You'll develop this intuition by exploring three different datasets&#58; soccer player positions, wholesale customer spending data, and longitudinal occupational wage data.

Intermediate R

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data. 

Cluster Analysis in R

In Chapter 1, you will learn how to conduct an EFA to examine the statistical properties of a measure designed around one construct.

Evaluating your measure with factor analysis

This chapter will show you how to extend the single-factor EFA you learned in Chapter 1 to multidimensional data.

Multidimensional EFA

This chapter will cover conducting CFAs with the sem package. Both theory-driven and EFA-driven CFA structures will be covered.

Confirmatory Factor Analysis

This chapter will reinforce the difference between EFAs and CFAs and offer suggestions for improving your model and/or measure.

Refining your measure and/or model

Generic Conspiracist Beliefs Scale (GCBS) dataset

The world is full of unobservable variables that can't be directly measured. You might be interested in a construct such as math ability, personality traits, or workplace climate. When investigating constructs like these, it's critically important to have a model that matches your theories and data. This course will help you understand dimensionality and show you how to conduct exploratory and confirmatory factor analyses. With these statistical techniques in your toolkit, you'll be able to develop, refine, and share your measures. These analyses are foundational for diverse fields including psychology, education, political science, economics, and linguistics.

Foundations of Inference

Explore latent variables, such as personality using exploratory and confirmatory factor analyses.

Factor Analysis in R

Probability & Statistics

Are you ready to become a master of dimensionality reduction?
In this chapter, you'll start by understanding how to represent handwritten digits using the MNIST dataset.  You will learn what a distance metric is and which ones are the most common, along with the problems that arise with the curse of dimensionality.
Finally, you will compare the application of PCA and t-SNE .

Introduction to Advanced Dimensionality Reduction 

Now, you will learn how to apply the t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. After finishing this chapter, you will understand the different hyperparameters that have an impact on your results and how to optimize them. Finally,  you will do something really cool: compute centroids prototypes of each digit to classify other digits.

Introduction to t-SNE

In this chapter, you'll apply t-SNE to train predictive models faster. This is one of the many advantages of dimensionality reduction. You will learn how to train a random forest with the original features and with the embedded features and compare them. You will also apply t-SNE to understand the patterns learned by a neural network. And all of this using a real credit card fraud dataset!

Using t-SNE with Predictive Models

In the final chapter, you will practice another useful dimensionality reduction algorithm: GLRM. Here you will make use of the Fashion MNIST data to classify clothes, impute missing data and also train random forests using the low dimensional embedding.

Generalized Low Rank Models (GLRM)

MNIST sample

Credit card fraud

Fashion MNIST sample

Dimensionality reduction techniques are based on unsupervised machine learning algorithms and their application offers several advantages. In this course you will learn how to apply dimensionality reduction techniques to exploit these advantages, using interesting datasets like the MNIST database of handwritten digits, the fashion version of MNIST released by Zalando, and a credit card fraud detection dataset. Firstly, you will have a look at t-SNE, an algorithm that performs non-linear dimensionality reduction. Then, you will also explore some useful characteristics of dimensionality reduction to apply in predictive models. Finally, you will see the application of GLRM to compress big data (with numerical and categorical values) and impute missing values. Are you ready to start compressing high dimensional data?

Dimensionality Reduction in R

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

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Unsupervised Machine Learning with R

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Join over 7,490,000 learners and start Unsupervised Machine Learning with R today!