Discover the skill sets required to implement various approaches to Machine Learning with Python

Key Features

• • Explore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and more.
  • Build your neural network models using modern Python libraries
  • Practical examples show you how to implement different machine learning and deep learning techniques

Book Description

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

This book discusses 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 machine and deep learning domains. You will explore various algorithms and techniques for implementing unsupervised learning in real-world use cases. You will learn various 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 in building and training a GAN to process images.

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

What you will learn

• • 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

Who this book is for

This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build intelligent applications by implementing key building blocks in unsupervised learning and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.

You might also be interested in my Technical Posts, which contain addenda and related topics.