Build your ultimate AI agent
Course Description
Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as Wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and SciPy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
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Start Course for FreeWhat you'll learn
- Assess intrinsic dimensionality by interpreting PCA explained-variance ratios and selecting optimal n_components for compression
- Distinguish between k-means, agglomerative hierarchical clustering, and t-SNE based on their algorithms, input requirements, and visualization outputs
- Evaluate cluster quality using inertia plots, dendrogram linkage distances, and cross-tabulations against known categories
- Identify appropriate preprocessing, clustering, and dimension-reduction tools in scikit-learn for specific unsupervised learning tasks
- Recognize significant latent features produced by NMF and apply cosine similarity to recommend documents or images with related topics or patterns
Prerequisites
Curriculum
Course outline
1
Clustering for Dataset Exploration
Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.
- Unsupervised Learning50 XP
- How many clusters?50 XP
- Clustering 2D points100 XP
- Inspect your clustering100 XP
- Evaluating a clustering50 XP
- How many clusters of grain?100 XP
- Evaluating the grain clustering100 XP
- Transforming features for better clusterings50 XP
- Scaling fish data for clustering100 XP
- Clustering the fish data100 XP
- Clustering stocks using KMeans100 XP
- Which stocks move together?100 XP
2
Visualization with Hierarchical Clustering and t-SNE
In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.
3
Decorrelating Your Data and Dimension Reduction
Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!
4
Discovering Interpretable Features
In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!
Unsupervised Learning in Python
Course
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