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The complete machine learning cource with Python

Author: Anthony Ng; Rob Percival
Publisher: [Place of publication not identified] : Packt Publishing, 2018.
Edition/Format:   eVideo : Clipart/images/graphics : English
Summary:
“Inside the course, you'll learn how to: set up a Python development environment correctly; gain complete machine learning toolsets to tackle most real-world problems; understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them; combine multiple models with by bagging, boosting or  Read more...
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Details

Material Type: Clipart/images/graphics, Internet resource, Videorecording
Document Type: Internet Resource, Computer File, Visual material
All Authors / Contributors: Anthony Ng; Rob Percival
OCLC Number: 1081335487
Notes: Title from resource description page (Safari, viewed January 8, 2019).
Performer(s): Presenter, Anthony NG.
Description: 1 online resource (1 streaming video file (18 hr., 22 min., 46 sec.)) : digital, sound, color
Responsibility: Anthony NG, Rob Percival.

Abstract:

“Inside the course, you'll learn how to: set up a Python development environment correctly; gain complete machine learning toolsets to tackle most real-world problems; understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them; combine multiple models with by bagging, boosting or stacking; make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data; develop in Jupyter (IPython) notebook, Spyder and various IDE; communicate visually and effectively with Matplotlib and Seaborn; engineer new features to improve algorithm predictions; make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data; use SVM for handwriting recognition, and classification problems in general; use decision trees to predict staff attrition; apply the association rule to retail shopping datasets.”—Resource description page.

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