• Complain

Aaron Jones - The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

Here you can read online Aaron Jones - The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. year: 2020, publisher: Packt Publishing, genre: Home and family. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

No cover
  • Book:
    The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
  • Author:
  • Publisher:
    Packt Publishing
  • Genre:
  • Year:
    2020
  • Rating:
    5 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 100
    • 1
    • 2
    • 3
    • 4
    • 5

The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginners workshop, featuring interesting examples and activities

Key Features
  • Get familiar with the ecosystem of unsupervised algorithms
  • Learn interesting methods to simplify large amounts of unorganized data
  • Tackle real-world challenges, such as estimating the population density of a geographical area
Book Description

Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner.

The book starts by introducing the most popular clustering algorithms of unsupervised learning. Youll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, youll use autoencoders for efficient data encoding.

As you progress, youll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, youll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area.

By the end of this book, youll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.

What you will learn
  • Distinguish between hierarchical clustering and the k-means algorithm
  • Understand the process of finding clusters in data
  • Grasp interesting techniques to reduce the size of data
  • Use autoencoders to decode data
  • Extract text from a large collection of documents using topic modeling
  • Create a bag-of-words model using the CountVectorizer
Who this book is for

If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as youll be editing classes and functions instead of creating them from scratch.

Table of Contents
  1. Introduction to Clustering
  2. Hierarchical Clustering
  3. Neighborhood Approaches and DBSCAN
  4. Dimensionality Reduction Techniques and PCA
  5. Autoencoders
  6. t-Distributed Stochastic Neighbor Embedding
  7. Topic Modeling
  8. Market Basket Analysis
  9. Hotspot Analysis

Aaron Jones: author's other books


Who wrote The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions? Find out the surname, the name of the author of the book and a list of all author's works by series.

The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
The UNSUPERVISED LEARNING Workshop Get started with unsupervised learning - photo 1
The
UNSUPERVISED LEARNING
Workshop

Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

Aaron Jones, Christopher Kruger, and Benjamin Johnston

The Unsupervised Learning Workshop

Copyright 2020 Packt Publishing

All rights reserved. No part of this course may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this course to ensure the accuracy of the information presented. However, the information contained in this course is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this course.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this course by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Authors: Aaron Jones, Christopher Kruger, and Benjamin Johnston

Reviewers: Richard Brooker, John Wesley Doyle, Priyanjit Ghosh, Sani Kamal, Ashish Pratik Patil, Geetank Raipuria, and Ratan Singh

Managing Editor: Rutuja Yerunkar

Acquisitions Editors: Manuraj Nair, Royluis Rodrigues, Anindya Sil, and Karan Wadekar

Production Editor: Salma Patel

Editorial Board: Megan Carlisle, Samuel Christa, Mahesh Dhyani, Heather Gopsill, Manasa Kumar, Alex Mazonowicz, Monesh Mirpuri, Bridget Neale, Dominic Pereira, Shiny Poojary, Abhishek Rane, Brendan Rodrigues, Erol Staveley, Ankita Thakur, Nitesh Thakur, and Jonathan Wray

First published: July 2020

Production reference: 1280720

ISBN: 978-1-80020-070-8

Published by Packt Publishing Ltd.

Livery Place, 35 Livery Street

Birmingham B3 2PB, UK

Table of Contents
Preface
About the Book

Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner.

The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding.

As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing Natural Language Processing. In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area.

By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.

Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions»

Look at similar books to The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions»

Discussion, reviews of the book The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.