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  ▼a  GBC094833
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  ▼a  019859997
  ▼2  Uk

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  ▼a  1180969905
  ▼a  1181834366
  ▼a  1197737347

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  ▼a  9781838823580
  ▼q  electronic book

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  ▼a  1838823581
  ▼q  electronic book

• 020

  ▼z  9781838826048

• 035

  ▼a  2562942
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1201697326
  ▼z  (OCoLC)1180969905
  ▼z  (OCoLC)1181834366
  ▼z  (OCoLC)1197737347

• 037

  ▼a  CL0501000160
  ▼b  Safari Books Online

• 040

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

• 050

  ▼a  Q325.5
  ▼b  .A57 2020

• 082

  ▼a  006.31
  ▼2  23

• 100

  ▼a  Amr, Tarek,
  ▼e  author.

• 245

  ▼a  Hands-on machine learning with scikit-learn and scientific Python toolkits:
  ▼b  a practical guide to implementing supervised and unsupervised machine learning algorithms in Python /:
  ▼c  Tarek Amr.

• 260

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

• 300

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

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼b  c
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 505

  ▼a  Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Supervised Learning -- Chapter 1: Introduction to Machine Learning -- Understanding machine learning -- Types of machine learning algorithms -- Supervised learning -- Classification versus regression -- Supervised learning evaluation -- Unsupervised learning -- Reinforcement learning -- The model development life cycle -- Understanding a problem -- Splitting our data -- Finding the best manner to split the data -- Making sure the training and the test datasets are separate

• 505

  ▼a  Development set -- Evaluating our model -- Deploying in production and monitoring -- Iterating -- When to use machine learning -- Introduction to scikit-learn -- It plays well with the Python data ecosystem -- Practical level of abstraction -- When not to use scikit-learn -- Installing the packages you need -- Introduction to pandas -- Python's scientific computing ecosystem conventions -- Summary -- Further reading -- Chapter 2: Making Decisions with Trees -- Understanding decision trees -- What are decision trees? -- Iris classification -- Loading the Iris dataset -- Splitting the data

• 505

  ▼a  Training the model and using it for prediction -- Evaluating our predictions -- Which features were more important? -- Displaying the internal tree decisions -- How do decision trees learn? -- Splitting criteria -- Preventing overfitting -- Predictions -- Getting a more reliable score -- What to do now to get a more reliable score -- ShuffleSplit -- Tuning the hyperparameters for higher accuracy -- Splitting the data -- Trying different hyperparameter values -- Comparing the accuracy scores -- Visualizing the tree's decision boundaries -- Feature engineering -- Building decision tree regressors

• 505

  ▼a  Predicting people's heights -- Regressor's evaluation -- Setting sample weights -- Summary -- Chapter 3: Making Decisions with Linear Equations -- Understanding linear models -- Linear equations -- Linear regression -- Estimating the amount paid to the taxi driver -- Predicting house prices in Boston -- Data exploration -- Splitting the data -- Calculating a baseline -- Training the linear regressor -- Evaluating our model's accuracy -- Showing feature coefficients -- Scaling for more meaningful coefficients -- Adding polynomial features -- Fitting the linear regressor with the derived features

• 505

  ▼a  Regularizing the regressor -- Training the lasso regressor -- Finding the optimum regularization parameter -- Finding regression intervals -- Getting to know additional linear regressors -- Using logistic regression for classification -- Understanding the logistic function -- Plugging the logistic function into a linear model -- Objective function -- Regularization -- Solvers -- Configuring the logistic regression classifier -- Classifying the Iris dataset using logistic regression -- Understanding the classifier's decision boundaries -- Getting to know additional linear classifiers -- Summary

• 520

  ▼a  This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.

• 588

  ▼a  Description based on online resource; title from digital title page (viewed on November 23, 2020).

• 590

  ▼a  OCLC control number change

• 650

  ▼a  Machine learning.

• 650

  ▼a  Python (Computer program language)

• 650

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

• 650

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

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Amr, Tarek
  ▼t  Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits : A Practical Guide to Implementing Supervised and Unsupervised Machine Learning Algorithms in Python.
  ▼d  Birmingham : Packt Publishing, Limited,c2020

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2562942

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  16873209

• 938

  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH37504100

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6270729

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2562942

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T