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)

Chapter 1

  1. Unsupervised learning can be applied independently from supervised approaches, because its goal is different. If a problem requires a supervised approach, often unsupervised learning cannot be employed as an alternative solution. In general, unsupervised methods try to extract pieces of information from a dataset (for example, clustering) without any external hint (such as the prediction error). Conversely, supervised methods require hints in order to correct their parameters.
  2. As the goal is finding the causes of the trend, it's necessary to perform a diagnostic analysis.
  3. No; the likelihood of n independent samples being drawn from the same distribution is obtained as a product of the single probabilities (see question 4 for the main assumption).
  4. The main hypothesis is that the samples are independent and identically distributed (IID).
  5. The gender can be encoded...