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Viser: Machine Learning with Python for Everyone

Machine Learning with Python for Everyone, 1. udgave

Machine Learning with Python for Everyone Vital Source e-bog

Mark Fenner
(2019)
Pearson International
494,00 kr. 444,60 kr.
Leveres umiddelbart efter køb
Machine Learning with Python for Everyone, 1. udgave

Machine Learning with Python for Everyone Vital Source e-bog

Mark Fenner
(2019)
Pearson International
238,00 kr.
Leveres umiddelbart efter køb
Machine Learning with Python for Everyone, 1. udgave

Machine Learning with Python for Everyone Vital Source e-bog

Mark Fenner
(2019)
Pearson International
340,00 kr.
Leveres umiddelbart efter køb
Machine Learning with Python for Everyone, 1. udgave

Machine Learning with Python for Everyone Vital Source e-bog

Mark Fenner
(2019)
Pearson International
170,00 kr.
Leveres umiddelbart efter køb
Machine Learning with Python for Everyone, 1. udgave

Machine Learning with Python for Everyone

Mark Fenner
(2019)
Addison Wesley Professional
459,00 kr.
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Detaljer Om Varen

  • 1. Udgave
  • Vital Source E-book
  • Udgiver: Pearson International (Juli 2019)
  • ISBN: 9780134845647
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Licens varighed:
Online udgaven er tilgængelig: 365 dage fra købsdato.
Offline udgaven er tilgængelig: ubegrænset dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
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Detaljer Om Varen

  • 1. Udgave
  • Vital Source leje e-bog 180 dage
  • Udgiver: Pearson International (Juli 2019)
  • ISBN: 9780134845647R180
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Licens varighed:
Online udgaven er tilgængelig: 180 dage fra købsdato.
Offline udgaven er tilgængelig: 180 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer Om Varen

  • 1. Udgave
  • Vital Source leje e-bog 365 dage
  • Udgiver: Pearson International (Juli 2019)
  • ISBN: 9780134845647R365
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Licens varighed:
Online udgaven er tilgængelig: 365 dage fra købsdato.
Offline udgaven er tilgængelig: 365 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer Om Varen

  • 1. Udgave
  • Vital Source leje e-bog 90 dage
  • Udgiver: Pearson International (Juli 2019)
  • ISBN: 9780134845647R90
The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Licens varighed:
Online udgaven er tilgængelig: 90 dage fra købsdato.
Offline udgaven er tilgængelig: 90 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer Om Varen

  • 1. Udgave
  • Paperback: 420 sider
  • Udgiver: Addison Wesley Professional (August 2019)
  • ISBN: 9780134845623
The Complete Beginner's Guide to Understanding and Building Machine Learning Systems with Python

Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you're an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.

Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use.

  • Understand machine learning algorithms, models, and core machine learning concepts
  • Classify examples with classifiers, and quantify examples with regressors
  • Realistically assess performance of machine learning systems
  • Use feature engineering to smooth rough data into useful forms
  • Chain multiple components into one system and tune its performance
  • Apply machine learning techniques to images and text
  • Connect the core concepts to neural networks and graphical models
  • Leverage the Python scikit-learn library and other powerful tools
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Chapter 1: Let's Discuss Learning
Chapter 2: Some Technical Background
Chapter 3: Predicting Categories: Getting Started with Classification
Chapter 4: Predicting Numerical Values: Getting Started with Regression

Part II: Evaluation
Chapter 5: Evaluating and Comparing Learners
Chapter 6: Evaluating Classifiers
Chapter 7: Evaluating Regressors

Part III: More Methods and Fundamentals
Chapter 8: More Classification Methods
Chapter 9: More Regression Methods
Chapter 10: Manual Feature Engineering: Manipulating Data for Fun and Profit
Chapter 11: Tuning Hyperparameters and Pipelines

Part IV: Adding Complexity
Chapter 12: Combining Learners
Chapter 13: Models That Engineer Features for Us
Chapter 14: Feature Engineering for Domains: Domain-Specific Learning
Chapter 15: Connections, Extensions, and Further Directions
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