The
Unsupervised
Learning
Workshop

Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner’s workshop, featuring interesting examples and activities.

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You'll be up and running with unsupervised learning in no time at all.

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A Smarter Way to Understand Unsupervised Learning

A step-by-step, focused approach to getting up and running with real-world unsupervised learning in no time at all.

The Unsupervised Learning Workshop is ideal if you're looking for a structured, hands-on approach to get started with unsupervised learning.

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Course Curriculum

An A to Z tour of unsupervised learning.

Workshop Onboarding
1. Introduction to Clustering
2. Hierarchical Clustering
- Overview
- Clustering Refresher
- The Organization of the Hierarchy
- Introduction to Hierarchical Clustering
- Exercise 2.01: Building a Hierarchy
- Linkage
- Exercise 2.02: Applying Linkage Criteria
- Agglomerative versus Divisive Clustering
- Exercise 2.03: Implementing Agglomerative Clustering with scikit-learn
- Activity 2.01: Comparing k-means with Hierarchical Clustering
- Activity 2.01: Comparing k-means with Hierarchical Clustering
- k-means versus Hierarchical Clustering
- Summary
- Survey I
3. Neighborhood Approaches and DBSCAN
- Overview
- Clusters as Neighborhoods
- Introduction to DBSCAN
- Exercise 3.01: Evaluating the Impact of Neighborhood Radius Size
- DBSCAN Attributes – Neighborhood Radius
- Activity 3.01: Implementing DBSCAN from Scratch
- Activity 3.01: Implementing DBSCAN from Scratch
- DBSCAN Attributes – Minimum Points
- Exercise 3.02: Evaluating the Impact of the Minimum Points Threshold
- Activity 3.02: Comparing DBSCAN with k-means and Hierarchical Clustering
- Activity 3.02: Comparing DBSCAN with k-means and Hierarchical Clustering
- DBSCAN versus k-means and Hierarchical Clustering
- Summary
4. Dimensionality Reduction Techniques and PCA
- Overview
- What Is Dimensionality Reduction?
- Overview of Dimensionality Reduction Techniques
- Principal Component Analysis
- Exercise 4.01: Computing Mean, Standard Deviation, and Variance Using the pandas Library
- Eigenvalues and Eigenvectors
- Exercise 4.02: Computing Eigenvalues and Eigenvectors
- The Process of PCA
- Exercise 4.03: Manually Executing PCA
- Exercise 4.04: scikit-learn PCA
- Activity 4.01: Manual PCA versus scikit-learn
- Activity 4.01: Manual PCA versus scikit-learn
- Restoring the Compressed Dataset
- Exercise 4.05: Visualizing Variance Reduction with Manual PCA
- Exercise 4.06: Visualizing Variance Reduction with scikit-learn
- Exercise 4.07: Plotting 3D Plots in Matplotlib
- Activity 4.02: PCA Using the Expanded Seeds Dataset
- Activity 4.02: PCA Using the Expanded Seeds Dataset
- Summary
5. Autoencoders
- Overview
- Fundamentals of Artificial Neural Networks
- Exercise 5.01: Modeling the Neurons of an Artificial Neural Network
- Exercise 5.02: Modeling Neurons with the ReLU Activation Function
- Neural Networks: Architecture Definition
- Exercise 5.03: Defining a Keras Model
- Neural Networks: Training
- Exercise 5.04: Training a Keras Neural Network Model
- Activity 5.01: The MNIST Neural Network
- Activity 5.01: The MNIST Neural Network
- Autoencoders
- Exercise 5.05: Simple Autoencoder
- Activity 5.02: Simple MNIST Autoencoder
- Activity 5.02: Simple MNIST Autoencoder
- Exercise 5.06: Multi-Layer Autoencoder
- Convolutional Neural Networks
- Exercise 5.07: Convolutional Autoencoder
- Activity 5.03: MNIST Convolutional Autoencoder
- Activity 5.03: MNIST Convolutional Autoencoder
- Summary
- Survey II
6. t-Distributed Stochastic Neighbor Embedding
- Overview
- The MNIST Dataset
- Stochastic Neighbor Embedding (SNE)
- Exercise 6.01: t-SNE MNIST
- Activity 6.01: Wine t-SNE
- Activity 6.01: Wine t-SNE
- Interpreting t-SNE Plots
- Exercise 6.02: t-SNE MNIST and Perplexity
- Activity 6.02: t-SNE Wine and Perplexity
- Activity 6.02: t-SNE Wine and Perplexity
- Exercise 6.03: t-SNE MNIST and Iterations
- Activity 6.03: t-SNE Wine and Iterations
- Activity 6.03: t-SNE Wine and Iterations
- Final Thoughts on Visualizations
- Summary
7. Topic Modeling
- Overview
- Topic Models
- Exercise 7.01: Setting up the Environment
- A High-Level Overview of Topic Models
- Exercise 7.02: Data Loading
- Cleaning Text Data
- Exercise 7.03: Cleaning Data Step by Step
- Exercise 7.04: Complete Data Cleaning
- Activity 7.01: Loading and Cleaning Twitter Data
- Activity 7.01: Loading and Cleaning Twitter Data
- Latent Dirichlet Allocation
- Exercise 7.05: Creating a Bag-of-Words Model Using the Count Vectorizer
- Perplexity
- Exercise 7.06: Selecting the Number of Topics
- Exercise 7.07: Running LDA
- Visualization
- Exercise 7.08: Visualizing LDA
- Exercise 7.09: Trying Four Topics
- Activity 7.02: LDA and Health Tweets
- Activity 7.02: LDA and Health Tweets
- Exercise 7.10: Creating a Bag-of-Words Model Using TF-IDF
- Non-Negative Matrix Factorization
- Exercise 7.11: Non-negative Matrix Factorization
- Exercise 7.12: Visualizing NMF
- Activity 7.03: Non-negative Matrix Factorization
- Activity 7.03: Non-negative Matrix Factorization
- Summary
8. Market Basket Analysis
- Overview
- Market Basket Analysis
- Exercise 8.01: Creating Sample Transaction Data
- Support
- Exercise 8.02: Computing Metrics
- Characteristics of Transaction Data
- Exercise 8.03: Loading Data
- Data Cleaning and Formatting
- Exercise 8.04: Data Cleaning and Formatting
- Data Encoding
- Exercise 8.05: Data Encoding
- Activity 8.01: Loading and Preparing Full Online Retail Data
- Activity 8.01: Loading and Preparing Full Online Retail Data
- The Apriori Algorithm
- Exercise 8.06: Executing the Apriori Algorithm
- Activity 8.02: Running the Apriori Algorithm on the Complete Online Retail Dataset
- Activity 8.02: Running the Apriori Algorithm on the Complete Online Retail Dataset
- Association Rules
- Exercise 8.07: Deriving Association Rules
- Activity 8.03: Finding the Association Rules on the Complete Online Retail Dataset
- Activity 8.03: Finding the Association Rules on the Complete Online Retail Dataset
- Summary
- Survey III
9. Hotspot Analysis
- Overview
- Spatial Statistics
- Kernel Density Estimation
- Exercise 9.01: The Effect of the Bandwidth Value
- Selecting the Optimal Bandwidth
- Exercise 9.02: Selecting the Optimal Bandwidth Using Grid Search
- Kernel Functions
- Exercise 9.03: The Effect of the Kernel Function
- Kernel Density Estimation Derivation
- Exercise 9.04: Simulating the Derivation of Kernel Density Estimation
- Activity 9.01: Estimating Density in One Dimension
- Activity 9.01: Estimating Density in One Dimension
- Hotspot Analysis
- Exercise 9.05: Loading Data and Modeling with Seaborn
- Exercise 9.06: Working with Basemaps
- Activity 9.02: Analyzing Crime in London
- Activity 9.02: Analyzing Crime in London
- Summary

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