The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
bLearning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities/bh4Key Features/h4ulliGet familiar with the ecosystem of unsupervised algorithms/liliLearn interesting me...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2020
|
| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bLearning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities/bh4Key Features/h4ulliGet familiar with the ecosystem of unsupervised algorithms/liliLearn interesting methods to simplify large amounts of unorganized data/liliTackle real-world challenges, such as estimating the population density of a geographical area/li/ulh4Book Description/h4Do 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 (NLP). 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.h4What you will learn/h4ulliDistinguish between hierarchical clustering and the k-means algorithm/liliUnderstand the process of finding clusters in data/liliGrasp interesting techniques to reduce the size of data/liliUse autoencoders to decode data/liliExtract text from a large collection of documents using topic modeling/liliCreate a bag-of-words model using the CountVectorizer/li/ulh4Who this book is for/h4If 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. |
| Beschreibung: | 1 Online-Ressource (550 Seiten) |
| ISBN: | 9781800206243 |
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| 520 | |a bLearning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities/bh4Key Features/h4ulliGet familiar with the ecosystem of unsupervised algorithms/liliLearn interesting methods to simplify large amounts of unorganized data/liliTackle real-world challenges, such as estimating the population density of a geographical area/li/ulh4Book Description/h4Do 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. | ||
| 520 | |a 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 (NLP). | ||
| 520 | |a 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.h4What you will learn/h4ulliDistinguish between hierarchical clustering and the k-means algorithm/liliUnderstand the process of finding clusters in data/liliGrasp interesting techniques to reduce the size of data/liliUse autoencoders to decode data/liliExtract text from a large collection of documents using topic modeling/liliCreate a bag-of-words model using the CountVectorizer/li/ulh4Who this book is for/h4If 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. | ||
| 650 | 4 | |a COMPUTERS / Neural Networks | |
| 650 | 4 | |a COMPUTERS / Programming Languages / Python | |
| 700 | 1 | |a Kruger, Christopher |e Sonstige |4 oth | |
| 700 | 1 | |a Johnston, Benjamin |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| id | DE-604.BV047069987 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:13:34Z |
| indexdate | 2024-07-10T09:01:44Z |
| institution | BVB |
| isbn | 9781800206243 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032477013 |
| oclc_num | 1227478873 |
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| physical | 1 Online-Ressource (550 Seiten) |
| psigel | ZDB-5-WPSE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
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| publisher | Packt Publishing Limited |
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| spelling | Jones, Aaron Verfasser aut The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions Jones, Aaron 1 Birmingham Packt Publishing Limited 2020 1 Online-Ressource (550 Seiten) txt rdacontent c rdamedia cr rdacarrier bLearning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities/bh4Key Features/h4ulliGet familiar with the ecosystem of unsupervised algorithms/liliLearn interesting methods to simplify large amounts of unorganized data/liliTackle real-world challenges, such as estimating the population density of a geographical area/li/ulh4Book Description/h4Do 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 (NLP). 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.h4What you will learn/h4ulliDistinguish between hierarchical clustering and the k-means algorithm/liliUnderstand the process of finding clusters in data/liliGrasp interesting techniques to reduce the size of data/liliUse autoencoders to decode data/liliExtract text from a large collection of documents using topic modeling/liliCreate a bag-of-words model using the CountVectorizer/li/ulh4Who this book is for/h4If 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. COMPUTERS / Neural Networks COMPUTERS / Programming Languages / Python Kruger, Christopher Sonstige oth Johnston, Benjamin Sonstige oth |
| spellingShingle | Jones, Aaron The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions COMPUTERS / Neural Networks COMPUTERS / Programming Languages / Python |
| title | The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions |
| title_auth | The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions |
| title_exact_search | The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions |
| title_exact_search_txtP | The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions |
| title_full | The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions Jones, Aaron |
| title_fullStr | The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions Jones, Aaron |
| title_full_unstemmed | The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions Jones, Aaron |
| title_short | The Unsupervised Learning Workshop |
| title_sort | the unsupervised learning workshop get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions |
| title_sub | Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions |
| topic | COMPUTERS / Neural Networks COMPUTERS / Programming Languages / Python |
| topic_facet | COMPUTERS / Neural Networks COMPUTERS / Programming Languages / Python |
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