Book Image

The Unsupervised Learning Workshop

By : Aaron Jones, Christopher Kruger, Benjamin Johnston
Book Image

The Unsupervised Learning Workshop

By: Aaron Jones, Christopher Kruger, Benjamin Johnston

Overview of this book

Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you’ll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you’ll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you’ll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.
Table of Contents (11 chapters)
Preface

The MNIST Dataset

Now, we will explore SNE and t-SNE using the MNIST dataset provided with the accompanying source code as the basis of our practical examples. Before we continue, we will quickly review MNIST and the data that is within it. The complete MNIST dataset is a collection of 60,000 training and 10,000 test examples of handwritten digits of the numbers 0 to 9, represented as black and white (or grayscale) images that are 28 x 28 pixels in size (giving 784 dimensions or features) with equal numbers of each type of digit (or class) in the dataset. Due to its size and the quality of the data, MNIST has become one of the quintessential datasets in machine learning, often being used as the reference dataset for many research papers in machine learning. One of the advantages of using MNIST to explore SNE and t-SNE compared to other datasets is that while the samples contain a high number of dimensions, they can be visualized even after dimensionality reduction because they can...