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Hands-On Unsupervised Learning with Python

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  • Use cluster algorithms to identify and optimize natural groups of data
  • Explore advanced non-linear and hierarchical clustering in action
  • Soft label assignments for fuzzy c-means and Gaussian mixture models
  • Detect anomalies through density estimation
  • Perform principal component analysis using neural network models
  • Create unsupervised models using GANs

Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.

This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.

By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.

  • Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more
  • Build your own neural network models using modern Python libraries
  • Practical examples show you how to implement different machine learning and deep learning techniques
Page Count 386
Course Length 11 hours 34 minutes
Date Of Publication 28 Feb 2019


Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.