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You have probably come across Google News, which automatically groups similar news articles under a topic. Have you ever wondered what process runs in the background to arrive at these groups? In this course, you will be introduced to unsupervised learning through clustering using the SciPy library in Python. This course covers pre-processing of data and application of hierarchical and k-means clustering. Through the course, you will explore player statistics from a popular football video game, FIFA 18. After completing the course, you will be able to quickly apply various clustering algorithms on data, visualize the clusters formed and analyze results.
Introduction to ClusteringFree
Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.Unsupervised learning: basics50 xpUnsupervised learning in real world50 xpPokémon sightings100 xpBasics of cluster analysis50 xpPokémon sightings: hierarchical clustering100 xpPokémon sightings: k-means clustering100 xpData preparation for cluster analysis50 xpNormalize basic list data100 xpVisualize normalized data100 xpNormalization of small numbers100 xpFIFA 18: Normalize data100 xp
This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.Basics of hierarchical clustering50 xpHierarchical clustering: ward method100 xpHierarchical clustering: single method100 xpHierarchical clustering: complete method100 xpVisualize clusters50 xpVisualize clusters with matplotlib100 xpVisualize clusters with seaborn100 xpHow many clusters?50 xpCreate a dendrogram100 xpHow many clusters in comic con data?50 xpLimitations of hierarchical clustering50 xpTiming run of hierarchical clustering50 xpFIFA 18: exploring defenders100 xp
This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.Basics of k-means clustering50 xpK-means clustering: first exercise100 xpRuntime of k-means clustering50 xpHow many clusters?50 xpElbow method on distinct clusters100 xpElbow method on uniform data100 xpLimitations of k-means clustering50 xpImpact of seeds on distinct clusters100 xpUniform clustering patterns100 xpFIFA 18: defenders revisited100 xp
Clustering in Real World
Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.Dominant colors in images50 xpExtract RGB values from image100 xpHow many dominant colors?100 xpDisplay dominant colors100 xpDocument clustering50 xpTF-IDF of movie plots100 xpTop terms in movie clusters100 xpClustering with multiple features50 xpClustering with many features50 xpBasic checks on clusters100 xpFIFA 18: what makes a complete player?100 xpFarewell!50 xp
In the following tracksMachine Learning Scientist with Python
Shaumik DaityariSee More
Business Analyst at American Express
Shaumik is a business analyst at American Express by day, and a comic book enthusiast by night (or maybe, he's Batman?) He has masters degrees from IIT Roorkee and IIM Lucknow, but apparently, none were as fun as coding in Python all day. Shaumik has been writing tutorials and creating screencasts for over five years. When not working, he's busy automating daily tasks through Python scripts!
Don’t just take our word for it
- Charles L.about 1 month
Covers many useful operations required in actual CA, which i believe will allow me to perform actual CA on my own.
- Sofia K.2 months
- David T.3 months
Great. Concise and precise
- Tyler J.7 months
- Pierre-Etienne T.7 months
This course is a good beginning about clustering analysis. Unfortunatelly, I wanted to learn advanced clustering algorithms like DBscan a spectrum analysis.