The Unsupervised Learning Workshop, 2nd Edition

Book Description

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

Key Features

  • Get familiar with the ecosystem of unsupervised algorithms
  • Learn interesting methods to simplify large amounts of unorganized data
  • Tackle real-world challenges, such as estimating the population density of a geographical area

Book Description

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 into a lower dimension for better visualization, in addition to working with topic for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket , before going on to use Hotspot 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.

What you will learn

  • Distinguish between hierarchical clustering and the k-means
  • Understand the process of finding clusters in data
  • Grasp interesting techniques to reduce the size of data
  • Use autoencoders to decode data
  • Extract text from a large collection of documents using topic modeling
  • Create a bag-of-words model using the CountVectorizer

Who this book is for

If you are a data scientist who is just getting started and want to learn how to implement algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.

Table of Contents

  1. Introduction to Clustering
  2. Hierarchical Clustering
  3. Neighborhood Approaches and DBSCAN
  4. Dimensionality Reduction Techniques and PCA
  5. Autoencoders
  6. t-Distributed Stochastic Neighbor Embedding
  7. Topic Modeling
  8. Market Basket Analysis
  9. Hotspot Analysis

Book Details

Download LinkFormatSize (MB)Upload Date
Download from NitroFlareCode26007/30/2020
Download from NitroFlareTrue PDF, EPUB, MOBI121.407/30/2020
Download from up-4everTrue PDF, EPUB, MOBI121.407/30/2020
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