The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

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Table of Contents
Preface
Chapter 1: Introduction to Clustering
	Introduction
	Unsupervised Learning versus Supervised Learning
	Clustering
		Identifying Clusters
		Two-Dimensional Data
		Exercise 1.01: Identifying Clusters in Data
	Introduction to k-means Clustering
		No-Math k-means Walkthrough
		K-means Clustering In-Depth Walkthrough
		Alternative Distance Metric – Manhattan Distance
		Deeper Dimensions
		Exercise 1.02: Calculating Euclidean Distance in Python
		Exercise 1.03: Forming Clusters with the Notion of Distance
		Exercise 1.04: K-means from Scratch – Part 1: Data Generation
		Exercise 1.05: K-means from Scratch – Part 2: Implementing k-means
		Clustering Performance – Silhouette Score
		Exercise 1.06: Calculating the Silhouette Score
		Activity 1.01: Implementing k-means Clustering
	Summary
Chapter 2: Hierarchical Clustering
	Introduction
	Clustering Refresher
		The k-means Refresher
	The Organization of the Hierarchy
	Introduction to Hierarchical Clustering
		Steps to Perform Hierarchical Clustering
		An Example Walkthrough of 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
	k-means versus Hierarchical Clustering
	Summary
Chapter 3: Neighborhood Approaches and DBSCAN
	Introduction
	Clusters as Neighborhoods
	Introduction to DBSCAN
		DBSCAN in Detail
		Walkthrough of the DBSCAN Algorithm
		Exercise 3.01: Evaluating the Impact of Neighborhood Radius Size
		DBSCAN Attributes – Neighborhood Radius
		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
	DBSCAN versus k-means and Hierarchical Clustering
	Summary
Chapter 4: Dimensionality Reduction Techniques and PCA
	Introduction
	What Is Dimensionality Reduction?
		Applications of Dimensionality Reduction
		The Curse of Dimensionality
	Overview of Dimensionality Reduction Techniques
		Dimensionality Reduction
	Principal Component Analysis
		Mean
		Standard Deviation
		Covariance
		Covariance Matrix
		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
		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
	Summary
Chapter 5: Autoencoders
	Introduction
	Fundamentals of Artificial Neural Networks
		The Neuron
		The Sigmoid Function
		Rectified Linear Unit (ReLU)
		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
	Autoencoders
		Exercise 5.05: Simple 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
	Summary
Chapter 6: t-Distributed Stochastic Neighbor Embedding
	Introduction
	The MNIST Dataset
	Stochastic Neighbor Embedding (SNE)
	t-Distributed SNE
		Exercise 6.01: t-SNE MNIST
		Activity 6.01: Wine t-SNE
	Interpreting t-SNE Plots
		Perplexity
		Exercise 6.02: t-SNE MNIST and Perplexity
		Activity 6.02: t-SNE Wine and Perplexity
		Iterations
		Exercise 6.03: t-SNE MNIST and Iterations
		Activity 6.03: t-SNE Wine and Iterations
		Final Thoughts on Visualizations
	Summary
Chapter 7: Topic Modeling
	Introduction
	Topic Models
		Exercise 7.01: Setting up the Environment
		A High-Level Overview of Topic Models
		Business Applications
		Exercise 7.02: Data Loading
	Cleaning Text Data
		Data Cleaning Techniques
		Exercise 7.03: Cleaning Data Step by Step
		Exercise 7.04: Complete Data Cleaning
		Activity 7.01: Loading and Cleaning Twitter Data
	Latent Dirichlet Allocation
		Variational Inference
		Bag of Words
		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
		Exercise 7.10: Creating a Bag-of-Words Model Using TF-IDF
	Non-Negative Matrix Factorization
		The Frobenius Norm
		The Multiplicative Update Algorithm
		Exercise 7.11: Non-negative Matrix Factorization
		Exercise 7.12: Visualizing NMF
		Activity 7.03: Non-negative Matrix Factorization
	Summary
Chapter 8: Market Basket Analysis
	Introduction
	Market Basket Analysis
		Use Cases
		Important Probabilistic Metrics
		Exercise 8.01: Creating Sample Transaction Data
		Support
		Confidence
		Lift and Leverage
		Conviction
		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
	The Apriori Algorithm
		Computational Fixes
		Exercise 8.06: Executing the Apriori Algorithm
		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
	Summary
Chapter 9: Hotspot Analysis
	Introduction
	Spatial Statistics
		Probability Density Functions
		Using Hotspot Analysis in Business
	Kernel Density Estimation
		The Bandwidth Value
		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
	Hotspot Analysis
		Exercise 9.05: Loading Data and Modeling with Seaborn
		Exercise 9.06: Working with Basemaps
		Activity 9.02: Analyzing Crime in London
	Summary
Appendix
Index