The Unsupervised Learning Workshop

The Unsupervised Learning Workshop, 2nd Edition

eBook Details:

  • Paperback: 550 pages
  • Publisher: WOW! eBook (July 29, 2020)
  • Language: English
  • ISBN-10: 1800200706
  • ISBN-13: 978-1800200708

eBook Description:

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

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, Second Edition 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.

  • Distinguish between hierarchical clustering and the k-means algorithm
  • 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

By the end of this The Unsupervised Learning Workshop, 2nd Edition book, you’ll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.


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