Machine Learning with Python Cookbook : practical solutions from preprocessing to deep learning / Kyle Gallatin & Chris Albon.

Format:

Book

Author/Creator:

Gallatin, Kyle.

Contributor:

Albon, Chris.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Data mining.

Physical Description:

1 online resource (416 p.)

Edition:

Second edition.

Place of Publication:

Sebastopol, CA : O'Reilly Media, Inc, 2023.

Summary:

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.

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

1.3 Creating a Sparse Matrix

1.4 Preallocating NumPy Arrays

1.5 Selecting Elements

Problem

Solution

1.6 Describing a Matrix

1.7 Applying Functions over Each Element

1.8 Finding the Maximum and Minimum Values

1.9 Calculating the Average, Variance, and Standard Deviation

1.10 Reshaping Arrays

1.11 Transposing a Vector or Matrix

1.12 Flattening a Matrix

1.13 Finding the Rank of a Matrix

1.14 Getting the Diagonal of a Matrix

1.15 Calculating the Trace of a Matrix

1.16 Calculating Dot Products

1.17 Adding and Subtracting Matrices

1.18 Multiplying Matrices

1.19 Inverting a Matrix

1.20 Generating Random Values

Discussion

Chapter 2. Loading Data

2.0 Introduction

2.1 Loading a Sample Dataset

2.2 Creating a Simulated Dataset

2.3 Loading a CSV File

2.4 Loading an Excel File

2.5 Loading a JSON File

2.6 Loading a Parquet File

2.7 Loading an Avro File

See Also

2.8 Querying a SQLite Database

2.9 Querying a Remote SQL Database

2.10 Loading Data from a Google Sheet

2.11 Loading Data from an S3 Bucket

2.12 Loading Unstructured Data

Chapter 3. Data Wrangling

3.0 Introduction

3.1 Creating a Dataframe

3.2 Getting Information about the Data

Notes:

OCLC-licensed vendor bibliographic record.

Description based upon print version of record.

Solution

ISBN:

9781098135713

1098135717

OCLC:

1391442069