Machine learning with Python cookbook: practical solutions from preprocessing to deep learning
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Beijing
O'Reilly
July 2023
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | FHD01 |
| Zusammenfassung: | This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Supporting vector machines (SVM), naṽe Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks |
| Beschreibung: | 1 Online-Ressource (xiv, 398 Seiten) |
| ISBN: | 9781098135690 |
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| 505 | 8 | |a Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Working with Vectors, Matrices, and Arrays in NumPy -- 1.0 Introduction -- 1.1 Creating a Vector -- Problem -- Solution -- Discussion -- See Also -- 1.2 Creating a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.3 Creating a Sparse Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.4 Preallocating NumPy Arrays -- Problem -- Solution -- Discussion -- 1.5 Selecting Elements -- Problem | |
| 505 | 8 | |a Solution -- Discussion -- 1.6 Describing a Matrix -- Problem -- Solution -- Discussion -- 1.7 Applying Functions over Each Element -- Problem -- Solution -- Discussion -- 1.8 Finding the Maximum and Minimum Values -- Problem -- Solution -- Discussion -- 1.9 Calculating the Average, Variance, and Standard Deviation -- Problem -- Solution -- Discussion -- 1.10 Reshaping Arrays -- Problem -- Solution -- Discussion -- 1.11 Transposing a Vector or Matrix -- Problem -- Solution -- Discussion -- 1.12 Flattening a Matrix -- Problem -- Solution -- Discussion -- 1.13 Finding the Rank of a Matrix -- Problem | |
| 505 | 8 | |a Solution -- Discussion -- See Also -- 1.14 Getting the Diagonal of a Matrix -- Problem -- Solution -- Discussion -- 1.15 Calculating the Trace of a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.16 Calculating Dot Products -- Problem -- Solution -- Discussion -- See Also -- 1.17 Adding and Subtracting Matrices -- Problem -- Solution -- Discussion -- 1.18 Multiplying Matrices -- Problem -- Solution -- Discussion -- See Also -- 1.19 Inverting a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.20 Generating Random Values -- Problem -- Solution -- Discussion | |
| 505 | 8 | |a Chapter 2. Loading Data -- 2.0 Introduction -- 2.1 Loading a Sample Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.2 Creating a Simulated Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.3 Loading a CSV File -- Problem -- Solution -- Discussion -- 2.4 Loading an Excel File -- Problem -- Solution -- Discussion -- 2.5 Loading a JSON File -- Problem -- Solution -- Discussion -- See Also -- 2.6 Loading a Parquet File -- Problem -- Solution -- Discussion -- See Also -- 2.7 Loading an Avro File -- Problem -- Solution -- Discussion -- See Also | |
| 505 | 8 | |a 2.8 Querying a SQLite Database -- Problem -- Solution -- Discussion -- See Also -- 2.9 Querying a Remote SQL Database -- Problem -- Solution -- Discussion -- See Also -- 2.10 Loading Data from a Google Sheet -- Problem -- Solution -- Discussion -- See Also -- 2.11 Loading Data from an S3 Bucket -- Problem -- Solution -- Discussion -- See Also -- 2.12 Loading Unstructured Data -- Problem -- Solution -- Discussion -- See Also -- Chapter 3. Data Wrangling -- 3.0 Introduction -- 3.1 Creating a Dataframe -- Problem -- Solution -- Discussion -- 3.2 Getting Information about the Data -- Problem | |
| 520 | |a This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Supporting vector machines (SVM), naṽe Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks | ||
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| contents | Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Working with Vectors, Matrices, and Arrays in NumPy -- 1.0 Introduction -- 1.1 Creating a Vector -- Problem -- Solution -- Discussion -- See Also -- 1.2 Creating a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.3 Creating a Sparse Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.4 Preallocating NumPy Arrays -- Problem -- Solution -- Discussion -- 1.5 Selecting Elements -- Problem Solution -- Discussion -- 1.6 Describing a Matrix -- Problem -- Solution -- Discussion -- 1.7 Applying Functions over Each Element -- Problem -- Solution -- Discussion -- 1.8 Finding the Maximum and Minimum Values -- Problem -- Solution -- Discussion -- 1.9 Calculating the Average, Variance, and Standard Deviation -- Problem -- Solution -- Discussion -- 1.10 Reshaping Arrays -- Problem -- Solution -- Discussion -- 1.11 Transposing a Vector or Matrix -- Problem -- Solution -- Discussion -- 1.12 Flattening a Matrix -- Problem -- Solution -- Discussion -- 1.13 Finding the Rank of a Matrix -- Problem Solution -- Discussion -- See Also -- 1.14 Getting the Diagonal of a Matrix -- Problem -- Solution -- Discussion -- 1.15 Calculating the Trace of a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.16 Calculating Dot Products -- Problem -- Solution -- Discussion -- See Also -- 1.17 Adding and Subtracting Matrices -- Problem -- Solution -- Discussion -- 1.18 Multiplying Matrices -- Problem -- Solution -- Discussion -- See Also -- 1.19 Inverting a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.20 Generating Random Values -- Problem -- Solution -- Discussion Chapter 2. Loading Data -- 2.0 Introduction -- 2.1 Loading a Sample Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.2 Creating a Simulated Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.3 Loading a CSV File -- Problem -- Solution -- Discussion -- 2.4 Loading an Excel File -- Problem -- Solution -- Discussion -- 2.5 Loading a JSON File -- Problem -- Solution -- Discussion -- See Also -- 2.6 Loading a Parquet File -- Problem -- Solution -- Discussion -- See Also -- 2.7 Loading an Avro File -- Problem -- Solution -- Discussion -- See Also 2.8 Querying a SQLite Database -- Problem -- Solution -- Discussion -- See Also -- 2.9 Querying a Remote SQL Database -- Problem -- Solution -- Discussion -- See Also -- 2.10 Loading Data from a Google Sheet -- Problem -- Solution -- Discussion -- See Also -- 2.11 Loading Data from an S3 Bucket -- Problem -- Solution -- Discussion -- See Also -- 2.12 Loading Unstructured Data -- Problem -- Solution -- Discussion -- See Also -- Chapter 3. Data Wrangling -- 3.0 Introduction -- 3.1 Creating a Dataframe -- Problem -- Solution -- Discussion -- 3.2 Getting Information about the Data -- Problem |
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| spelling | Gallatin, Kyle Verfasser aut Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon Second edition Beijing O'Reilly July 2023 1 Online-Ressource (xiv, 398 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Working with Vectors, Matrices, and Arrays in NumPy -- 1.0 Introduction -- 1.1 Creating a Vector -- Problem -- Solution -- Discussion -- See Also -- 1.2 Creating a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.3 Creating a Sparse Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.4 Preallocating NumPy Arrays -- Problem -- Solution -- Discussion -- 1.5 Selecting Elements -- Problem Solution -- Discussion -- 1.6 Describing a Matrix -- Problem -- Solution -- Discussion -- 1.7 Applying Functions over Each Element -- Problem -- Solution -- Discussion -- 1.8 Finding the Maximum and Minimum Values -- Problem -- Solution -- Discussion -- 1.9 Calculating the Average, Variance, and Standard Deviation -- Problem -- Solution -- Discussion -- 1.10 Reshaping Arrays -- Problem -- Solution -- Discussion -- 1.11 Transposing a Vector or Matrix -- Problem -- Solution -- Discussion -- 1.12 Flattening a Matrix -- Problem -- Solution -- Discussion -- 1.13 Finding the Rank of a Matrix -- Problem Solution -- Discussion -- See Also -- 1.14 Getting the Diagonal of a Matrix -- Problem -- Solution -- Discussion -- 1.15 Calculating the Trace of a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.16 Calculating Dot Products -- Problem -- Solution -- Discussion -- See Also -- 1.17 Adding and Subtracting Matrices -- Problem -- Solution -- Discussion -- 1.18 Multiplying Matrices -- Problem -- Solution -- Discussion -- See Also -- 1.19 Inverting a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.20 Generating Random Values -- Problem -- Solution -- Discussion Chapter 2. Loading Data -- 2.0 Introduction -- 2.1 Loading a Sample Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.2 Creating a Simulated Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.3 Loading a CSV File -- Problem -- Solution -- Discussion -- 2.4 Loading an Excel File -- Problem -- Solution -- Discussion -- 2.5 Loading a JSON File -- Problem -- Solution -- Discussion -- See Also -- 2.6 Loading a Parquet File -- Problem -- Solution -- Discussion -- See Also -- 2.7 Loading an Avro File -- Problem -- Solution -- Discussion -- See Also 2.8 Querying a SQLite Database -- Problem -- Solution -- Discussion -- See Also -- 2.9 Querying a Remote SQL Database -- Problem -- Solution -- Discussion -- See Also -- 2.10 Loading Data from a Google Sheet -- Problem -- Solution -- Discussion -- See Also -- 2.11 Loading Data from an S3 Bucket -- Problem -- Solution -- Discussion -- See Also -- 2.12 Loading Unstructured Data -- Problem -- Solution -- Discussion -- See Also -- Chapter 3. Data Wrangling -- 3.0 Introduction -- 3.1 Creating a Dataframe -- Problem -- Solution -- Discussion -- 3.2 Getting Information about the Data -- Problem This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Supporting vector machines (SVM), naṽe Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Albon, Chris Verfasser (DE-588)1165271796 aut Erscheint auch als Druck-Ausgabe 978-1-098-13572-0 |
| spellingShingle | Gallatin, Kyle Albon, Chris Machine learning with Python cookbook practical solutions from preprocessing to deep learning Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Working with Vectors, Matrices, and Arrays in NumPy -- 1.0 Introduction -- 1.1 Creating a Vector -- Problem -- Solution -- Discussion -- See Also -- 1.2 Creating a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.3 Creating a Sparse Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.4 Preallocating NumPy Arrays -- Problem -- Solution -- Discussion -- 1.5 Selecting Elements -- Problem Solution -- Discussion -- 1.6 Describing a Matrix -- Problem -- Solution -- Discussion -- 1.7 Applying Functions over Each Element -- Problem -- Solution -- Discussion -- 1.8 Finding the Maximum and Minimum Values -- Problem -- Solution -- Discussion -- 1.9 Calculating the Average, Variance, and Standard Deviation -- Problem -- Solution -- Discussion -- 1.10 Reshaping Arrays -- Problem -- Solution -- Discussion -- 1.11 Transposing a Vector or Matrix -- Problem -- Solution -- Discussion -- 1.12 Flattening a Matrix -- Problem -- Solution -- Discussion -- 1.13 Finding the Rank of a Matrix -- Problem Solution -- Discussion -- See Also -- 1.14 Getting the Diagonal of a Matrix -- Problem -- Solution -- Discussion -- 1.15 Calculating the Trace of a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.16 Calculating Dot Products -- Problem -- Solution -- Discussion -- See Also -- 1.17 Adding and Subtracting Matrices -- Problem -- Solution -- Discussion -- 1.18 Multiplying Matrices -- Problem -- Solution -- Discussion -- See Also -- 1.19 Inverting a Matrix -- Problem -- Solution -- Discussion -- See Also -- 1.20 Generating Random Values -- Problem -- Solution -- Discussion Chapter 2. Loading Data -- 2.0 Introduction -- 2.1 Loading a Sample Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.2 Creating a Simulated Dataset -- Problem -- Solution -- Discussion -- See Also -- 2.3 Loading a CSV File -- Problem -- Solution -- Discussion -- 2.4 Loading an Excel File -- Problem -- Solution -- Discussion -- 2.5 Loading a JSON File -- Problem -- Solution -- Discussion -- See Also -- 2.6 Loading a Parquet File -- Problem -- Solution -- Discussion -- See Also -- 2.7 Loading an Avro File -- Problem -- Solution -- Discussion -- See Also 2.8 Querying a SQLite Database -- Problem -- Solution -- Discussion -- See Also -- 2.9 Querying a Remote SQL Database -- Problem -- Solution -- Discussion -- See Also -- 2.10 Loading Data from a Google Sheet -- Problem -- Solution -- Discussion -- See Also -- 2.11 Loading Data from an S3 Bucket -- Problem -- Solution -- Discussion -- See Also -- 2.12 Loading Unstructured Data -- Problem -- Solution -- Discussion -- See Also -- Chapter 3. Data Wrangling -- 3.0 Introduction -- 3.1 Creating a Dataframe -- Problem -- Solution -- Discussion -- 3.2 Getting Information about the Data -- Problem Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4434275-5 |
| title | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_auth | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_exact_search | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_exact_search_txtP | Machine learning with Python cookbook practical solutions from preprocessing to deep learning |
| title_full | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon |
| title_fullStr | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon |
| title_full_unstemmed | Machine learning with Python cookbook practical solutions from preprocessing to deep learning Kyle Gallatin and Chris Albon |
| title_short | Machine learning with Python cookbook |
| title_sort | machine learning with python cookbook practical solutions from preprocessing to deep learning |
| title_sub | practical solutions from preprocessing to deep learning |
| topic | Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) Maschinelles Lernen Python Programmiersprache |
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