Machine Learning with Python Cookbook

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.

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 -- Solution -- Discussion -- 3.3 Slicing DataFrames -- Problem -- Solution -- Discussion -- 3.4 Selecting Rows Based on Conditionals -- Problem -- Solution -- Discussion -- 3.5 Sorting Values -- Problem -- Solution -- Discussion -- 3.6 Replacing Values -- Problem -- Solution -- Discussion -- 3.7 Renaming Columns -- Problem -- Solution -- Discussion -- 3.8 Finding the Minimum, Maximum, Sum, Average, and Count -- Problem -- Solution -- Discussion -- 3.9 Finding Unique Values -- Problem -- Solution -- Discussion -- 3.10 Handling Missing Values -- Problem -- Solution -- Discussion -- 3.11 Deleting a Column -- Problem -- Solution -- Discussion -- 3.12 Deleting a Row -- Problem -- Solution -- Discussion -- 3.13 Dropping Duplicate Rows -- Problem -- Solution -- Discussion.

3.14 Grouping Rows by Values -- Problem -- Solution -- Discussion -- 3.15 Grouping Rows by Time -- Problem -- Solution -- Discussion -- See Also -- 3.16 Aggregating Operations and Statistics -- Problem -- Solution -- Discussion -- See Also -- 3.17 Looping over a Column -- Problem -- Solution -- Discussion -- 3.18 Applying a Function over All Elements in a Column -- Problem -- Solution -- Discussion -- 3.19 Applying a Function to Groups -- Problem -- Solution -- Discussion -- 3.20 Concatenating DataFrames -- Problem -- Solution -- Discussion -- 3.21 Merging DataFrames -- Problem -- Solution -- Discussion -- See Also -- Chapter 4. Handling Numerical Data -- 4.0 Introduction -- 4.1 Rescaling a Feature -- Problem -- Solution -- Discussion -- See Also -- 4.2 Standardizing a Feature -- Problem -- Solution -- Discussion -- 4.3 Normalizing Observations -- Problem -- Solution -- Discussion -- 4.4 Generating Polynomial and Interaction Features -- Problem -- Solution -- Discussion -- 4.5 Transforming Features -- Problem -- Solution -- Discussion -- 4.6 Detecting Outliers -- Problem -- Solution -- Discussion -- See Also -- 4.7 Handling Outliers -- Problem -- Solution -- Discussion -- See Also -- 4.8 Discretizating Features -- Problem -- Solution -- Discussion -- See Also -- 4.9 Grouping Observations Using Clustering -- Problem -- Solution -- Discussion -- 4.10 Deleting Observations with Missing Values -- Problem -- Solution -- Discussion -- See Also -- 4.11 Imputing Missing Values -- Problem -- Solution -- Discussion -- See Also -- Chapter 5. Handling Categorical Data -- 5.0 Introduction -- 5.1 Encoding Nominal Categorical Features -- Problem -- Solution -- Discussion -- See Also -- 5.2 Encoding Ordinal Categorical Features -- Problem -- Solution -- Discussion -- 5.3 Encoding Dictionaries of Features -- Problem -- Solution -- Discussion -- See Also.

5.4 Imputing Missing Class Values -- Problem -- Solution -- Discussion -- See Also -- 5.5 Handling Imbalanced Classes -- Problem -- Solution -- Discussion -- Chapter 6. Handling Text -- 6.0 Introduction -- 6.1 Cleaning Text -- Problem -- Solution -- Discussion -- See Also -- 6.2 Parsing and Cleaning HTML -- Problem -- Solution -- Discussion -- See Also -- 6.3 Removing Punctuation -- Problem -- Solution -- Discussion -- 6.4 Tokenizing Text -- Problem -- Solution -- Discussion -- 6.5 Removing Stop Words -- Problem -- Solution -- Discussion -- 6.6 Stemming Words -- Problem -- Solution -- Discussion -- See Also -- 6.7 Tagging Parts of Speech -- Problem -- Solution -- Discussion -- See Also -- 6.8 Performing Named-Entity Recognition -- Problem -- Solution -- Discussion -- See Also -- 6.9 Encoding Text as a Bag of Words -- Problem -- Solution -- Discussion -- See Also -- 6.10 Weighting Word Importance -- Problem -- Solution -- Discussion -- See Also -- 6.11 Using Text Vectors to Calculate Text Similarity in a Search Query -- Problem -- Solution -- Discussion -- See Also -- 6.12 Using a Sentiment Analysis Classifier -- Problem -- Solution -- Discussion -- See Also -- Chapter 7. Handling Dates and Times -- 7.0 Introduction -- 7.1 Converting Strings to Dates -- Problem -- Solution -- Discussion -- See Also -- 7.2 Handling Time Zones -- Problem -- Solution -- Discussion -- 7.3 Selecting Dates and Times -- Problem -- Solution -- Discussion -- 7.4 Breaking Up Date Data into Multiple Features -- Problem -- Solution -- Discussion -- 7.5 Calculating the Difference Between Dates -- Problem -- Solution -- Discussion -- See Also -- 7.6 Encoding Days of the Week -- Problem -- Solution -- Discussion -- See Also -- 7.7 Creating a Lagged Feature -- Problem -- Solution -- Discussion -- 7.8 Using Rolling Time Windows -- Problem -- Solution -- Discussion -- See Also.

7.9 Handling Missing Data in Time Series -- Problem -- Solution -- Discussion -- Chapter 8. Handling Images -- 8.0 Introduction -- 8.1 Loading Images -- Problem -- Solution -- Discussion -- See Also -- 8.2 Saving Images -- Problem -- Solution -- Discussion -- 8.3 Resizing Images -- Problem -- Solution -- Discussion -- 8.4 Cropping Images -- Problem -- Solution -- Discussion -- See Also -- 8.5 Blurring Images -- Problem -- Solution -- Discussion -- See Also -- 8.6 Sharpening Images -- Problem -- Solution -- Discussion -- 8.7 Enhancing Contrast -- Problem -- Solution -- Discussion -- 8.8 Isolating Colors -- Problem -- Solution -- Discussion -- 8.9 Binarizing Images -- Problem -- Solution -- Discussion -- 8.10 Removing Backgrounds -- Problem -- Solution -- Discussion -- 8.11 Detecting Edges -- Problem -- Solution -- Discussion -- See Also -- 8.12 Detecting Corners -- Problem -- Solution -- Discussion -- See Also -- 8.13 Creating Features for Machine Learning -- Problem -- Solution -- Discussion -- 8.14 Encoding Color Histograms as Features -- Problem -- Solution -- Discussion -- See Also -- 8.15 Using Pretrained Embeddings as Features -- Problem -- Solution -- Discussion -- See Also -- 8.16 Detecting Objects with OpenCV -- Problem -- Solution -- Discussion -- See Also -- 8.17 Classifying Images with Pytorch -- Problem -- Solution -- Discussion -- See Also -- Chapter 9. Dimensionality Reduction Using Feature Extraction -- 9.0 Introduction -- 9.1 Reducing Features Using Principal Components -- Problem -- Solution -- Discussion -- See Also -- 9.2 Reducing Features When Data Is Linearly Inseparable -- Problem -- Solution -- Discussion -- See Also -- 9.3 Reducing Features by Maximizing Class Separability -- Problem -- Solution -- Discussion -- See Also -- 9.4 Reducing Features Using Matrix Factorization -- Problem -- Solution -- Discussion -- See Also.