Book Image

Hands-On Unsupervised Learning with Python

By : Giuseppe Bonaccorso
Book Image

Hands-On Unsupervised Learning with Python

By: Giuseppe Bonaccorso

Overview of this book

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.
Table of Contents (12 chapters)


  1. What's the main difference between soft and hard clustering?
  2. Fuzzy c-means can easily deal with non-convex clusters. Is this statement correct?
  3. Which is the main assumption of a Gaussian mixture?
  4. Suppose that two models achieve the same optimal log-likelihood; however, the first one has an AIC that is double the second one. What does this mean?
  5. Considering the previous question, which model would we prefer?
  6. Why would we want to employ the Dirichlet distribution as the prior for the weights of a Bayesian Gaussian mixture?
  7. Suppose that we have a dataset containing 1,000 labeled samples, whose values have been certified by an expert. We collect 5,000 samples from the same sources, but we don't want to pay for extra labeling. What can we do in order to incorporate them into our model?