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  201027s2020 enka o 000 0 eng d

• 019

  ▼a  1178714596
  ▼a  1181842248
  ▼a  1191043302

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  ▼a  9781838985462

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  ▼a  1838985468

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  ▼z  9781839219061

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  ▼a  2532421
  ▼b  (N$T)

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  ▼a  (OCoLC)1201697296
  ▼z  (OCoLC)1178714596
  ▼z  (OCoLC)1181842248
  ▼z  (OCoLC)1191043302

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  ▼a  CL0501000160
  ▼b  Safari Books Online

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  ▼a  UMI
  ▼b  eng
  ▼e  rda
  ▼e  pn
  ▼c  UMI
  ▼d  EBLCP
  ▼d  UKAHL
  ▼d  YDX
  ▼d  N$T
  ▼d  OCLCF
  ▼d  248023

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  ▼a  QA76.87

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  ▼a  006.31
  ▼2  23

• 100

  ▼a  Saleh, Hyatt,
  ▼e  author.

• 245

  ▼a  The machine learning workshop.

• 250

  ▼a  Second edition.

• 260

  ▼a  Birmingham, UK:
  ▼b  Packt Publishing,
  ▼c  2020.

• 300

  ▼a  1 online resource (1 volume):
  ▼b  illustrations.

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

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  ▼a  computer
  ▼b  c
  ▼2  rdamedia

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  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 505

  ▼a  Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Scikit-Learn -- Introduction -- Introduction to Machine Learning -- Applications of ML -- Choosing the Right ML Algorithm -- Scikit-Learn -- Advantages of Scikit-Learn -- Disadvantages of Scikit-Learn -- Other Frameworks -- Data Representation -- Tables of Data -- Features and Target Matrices -- Exercise 1.01: Loading a Sample Dataset and Creating the Features and Target Matrices -- Activity 1.01: Selecting a Target Feature and Creating a Target Matrix -- Data Preprocessing -- Messy Data -- Missing Values

• 505

  ▼a  Outliers -- Exercise 1.02: Dealing with Messy Data -- Dealing with Categorical Features -- Feature Engineering -- Exercise 1.03: Applying Feature Engineering to Text Data -- Rescaling Data -- Exercise 1.04: Normalizing and Standardizing Data -- Activity 1.02: Pre-processing an Entire Dataset -- Scikit-Learn API -- How Does It Work? -- Estimator -- Predictor -- Transformer -- Supervised and Unsupervised Learning -- Supervised Learning -- Unsupervised Learning -- Summary -- Chapter 2: Unsupervised Learning -- Real-Life Applications -- Introduction -- Clustering -- Clustering Types

• 505

  ▼a  Applications of Clustering -- Exploring a Dataset -- Wholesale Customers Dataset -- Understanding the Dataset -- Data Visualization -- Loading the Dataset Using pandas -- Visualization Tools -- Exercise 2.01: Plotting a Histogram of One Feature from the Circles Dataset -- Activity 2.01: Using Data Visualization to Aid the Pre-processing Process -- k-means Algorithm -- Understanding the Algorithm -- Initialization Methods -- Choosing the Number of Clusters -- Exercise 2.02: Importing and Training the k-means Algorithm over a Dataset -- Activity 2.02: Applying the k-means Algorithm to a Dataset

• 505

  ▼a  Mean-Shift Algorithm -- Understanding the Algorithm -- Exercise 2.03: Importing and Training the Mean-Shift Algorithm over a Dataset -- Activity 2.03: Applying the Mean-Shift Algorithm to a Dataset -- DBSCAN Algorithm -- Understanding the Algorithm -- Exercise 2.04: Importing and Training the DBSCAN Algorithm over a Dataset -- Activity 2.04: Applying the DBSCAN Algorithm to the Dataset -- Evaluating the Performance of Clusters -- Available Metrics in Scikit-Learn -- Exercise 2.05: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index

• 505

  ▼a  Activity 2.05: Measuring and Comparing the Performance of the Algorithms -- Summary -- Chapter 3: Supervised Learning -- Key Steps -- Introduction -- Supervised Learning Tasks -- Model Validation and Testing -- Data Partitioning -- Split Ratio -- Exercise 3.01: Performing a Data Partition on a Sample Dataset -- Cross-Validation -- Exercise 3.02: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set -- Activity 3.01: Data Partitioning on a Handwritten Digit Dataset -- Evaluation Metrics -- Evaluation Metrics for Classification Tasks -- Confusion Matrix -- Accuracy

• 520

  ▼a  With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems.

• 588

  ▼a  Description based on online resource; title from title page (viewed October 22, 2020).

• 590

  ▼a  OCLC control number change

• 650

  ▼a  Machine learning.

• 650

  ▼a  Neural networks (Computer science)

• 650

  ▼a  Artificial intelligence.

• 650

  ▼a  Machine learning
  ▼2  fast
  ▼0  (OCoLC)fst01004795

• 650

  ▼a  Python (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01084736

• 655

  ▼a  Electronic books.

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Saleh, Hyatt
  ▼t  The the Machine Learning Workshop : Get Ready to Develop Your Own High-Performance Machine Learning Algorithms with Scikit-learn, 2nd Edition.
  ▼d  Birmingham : Packt Publishing, Limited,c2020

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  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2532421

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  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6269367

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  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH37507361

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  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  301401445

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  ▼a  EBSCOhost
  ▼b  EBSC
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  ▼a  강리원

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  ▼a  eBook

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