Machine Learning for Absolute Beginners: A Plain English Introduction
Machine Learning for Absolute Beginners: A Plain English Introduction
  • Išparduota
In This Step-By-Step Guide You Will Learn • How to download free datasets • What tools and machine learning libraries you need • Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data • Preparing data for analysis, including k-fold Validation • Regression analysis to create trend lines • k-Means Clustering to find new relationships • The basics of Neural Networks • Bias/Variance to improve your machine learning model • Decision Trees to decode clas…
  • Metai: 2020
  • Puslapiai: 181
  • ISBN: 9781549617218
  • Formatas: 15,5 x 23 cm, minkšti viršeliai
  • Kalba: Anglų

Machine Learning for Absolute Beginners: A Plain English Introduction (el. knyga) (skaityta knyga) | knygos.lt

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Aprašymas

In This Step-By-Step Guide You Will Learn

• How to download free datasets

• What tools and machine learning libraries you need

• Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data

• Preparing data for analysis, including k-fold Validation

• Regression analysis to create trend lines

• k-Means Clustering to find new relationships

• The basics of Neural Networks

• Bias/Variance to improve your machine learning model

• Decision Trees to decode classification, and

• How to build your first Machine Learning Model to predict house values using Python

Išparduota

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  • Autorius: Oliver Theobald
  • Metai: 2020
  • Puslapiai: 181
  • ISBN: 9781549617218
  • Formatas: 15,5 x 23 cm, minkšti viršeliai
  • Kalba: Anglų

In This Step-By-Step Guide You Will Learn

• How to download free datasets

• What tools and machine learning libraries you need

• Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data

• Preparing data for analysis, including k-fold Validation

• Regression analysis to create trend lines

• k-Means Clustering to find new relationships

• The basics of Neural Networks

• Bias/Variance to improve your machine learning model

• Decision Trees to decode classification, and

• How to build your first Machine Learning Model to predict house values using Python

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