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Hands-On Unsupervised Learning with Python
Hands-On Unsupervised Learning with Python

Hands-On Unsupervised Learning with Python: Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

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Profile Icon Giuseppe Bonaccorso Profile Icon Giuseppe Bonaccorso
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$51.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
Paperback Feb 2019 386 pages 1st Edition
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Arrow left icon
Profile Icon Giuseppe Bonaccorso Profile Icon Giuseppe Bonaccorso
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$51.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (3 Ratings)
Paperback Feb 2019 386 pages 1st Edition
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$33.99 $37.99
Paperback
$51.99
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Renews at $19.99p/m
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Hands-On Unsupervised Learning with Python

Getting Started with Unsupervised Learning

In this chapter, we are going to introduce fundamental machine learning concepts, assuming that you have some basic knowledge of statistical learning and probability theory. You'll learn about the uses of machine learning techniques and the logical process that improves our knowledge about both nature and the properties of a dataset. The purpose of the entire process is to build descriptive and predictive models the can support business decisions.

Unsupervised learning aims to provide tools for data exploration, mining, and generation. In this book, you'll explore different scenarios with concrete examples and analyses, and you'll learn how to apply fundamental and more complex algorithms to solve specific problems.

In this introductory chapter, we are going to discuss:

  • Why do we need machine learning?
  • Descriptive, diagnostic, predictive, and prescriptive analyses
  • Types of machine learning
  • Why are we using Python?

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Key benefits

  • 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

Description

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.

Who is this book for?

This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block 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.

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
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Publication date : Feb 28, 2019
Length: 386 pages
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Language : English
ISBN-13 : 9781789348279
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ISBN-13 : 9781789348279
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Frequently bought together


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Total $ 139.97
Applied Unsupervised Learning with Python
$48.99
Hands-On Unsupervised Learning with Python
$51.99
Python Machine Learning By Example
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Total $ 139.97 Stars icon

Table of Contents

11 Chapters
Getting Started with Unsupervised Learning Chevron down icon Chevron up icon
Clustering Fundamentals Chevron down icon Chevron up icon
Advanced Clustering Chevron down icon Chevron up icon
Hierarchical Clustering in Action Chevron down icon Chevron up icon
Soft Clustering and Gaussian Mixture Models Chevron down icon Chevron up icon
Anomaly Detection Chevron down icon Chevron up icon
Dimensionality Reduction and Component Analysis Chevron down icon Chevron up icon
Unsupervised Neural Network Models Chevron down icon Chevron up icon
Generative Adversarial Networks and SOMs Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 0%
1 star 33.3%
Ellery Lin May 24, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I appreciate the introduction of Shape - to cluster time-series very much.
Amazon Verified review Amazon
Diana Sep 08, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I like that have the theory and examples in how to program it in Python. This book who's a little bit about mathematics equations. So if you are interested in the demonstration of mathematics equations then you need other book. This is practical book in Python and I love it.
Amazon Verified review Amazon
Danielle W Jun 08, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
This is a paperback re-print made in black and white. Nowhere in the description or preview does it show that it's not in color. For most of the figures having color is pretty important to follow along. Disappointing.
Amazon Verified review Amazon
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