Machine Learning with Python Cookbook, 2nd Edition (Second Early Release)

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 all the way 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 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. NumPy is a foundational tool of the Python machine learning stack. NumPy allows for efficient operations on the data structures often used in machine learning: vectors, matrices, and tensors. While NumPy is not the focus of this book, it will show up frequently throughout the following chapters. This chapter covers the most common NumPy operations we are likely to run into while working on Machine Learning workflows. 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 Support vector machines (SVM), naive Bayes, clustering, and tree-based models Saving and loading trained models from multiple frameworks 1.0. Introduction 1.1. Creating a Vector 1.2. Creating a Matrix 1.3. Creating a Sparse Matrix 1.4. Pre-allocating Numpy Arrays 1.5. Selecting Elements 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 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 a avro file 2.8. Loading a TFRecord file 2.9. Querying a SQLite Database 2.10. Querying a Remote SQL Database 2.11. Loading Data from a Google Sheet 2.12. Loading Data from an S3 Bucket 2.13. Loading Unstructured Data 3. Data Wrangling 3.0. Introduction 3.1. Creating a Data Frame 3.2. Getting Information about the Data 3.3. Slicing DataFrames 3.4. Selecting Rows Based on Conditionals 3.5. Sorting Values 3.6. Replacing Values 3.7. Renaming Columns 3.8. Finding the Minimum, Maximum, Sum, Average, and Count 3.9. Finding Unique Values 3.10. Handling Missing Values 3.11. Deleting a Column 3.12. Deleting a Row 3.13. Dropping Duplicate Rows 3.14. Grouping Rows by Values 3.15. Grouping Rows by Time 3.16. Aggregating Operations and Statistics 3.17. Looping Over a Column 3.18. Applying a Function Over All Elements in a Column 3.19. Applying a Function to Groups 3.20. Concatenating DataFrames 3.21. Merging DataFrames 4. Handling Numerical Data 4.0. Introduction 4.1. Rescaling a Feature 4.2. Standardizing a Feature 4.3. Normalizing Observations 4.4. Generating Polynomial and Interaction Features 4.5. Transforming Features 4.6. Detecting Outliers 4.7. Handling Outliers 4.8. Discretizating Features 4.9. Grouping Observations Using Clustering 4.10. Deleting Observations with Missing Values 4.11. Imputing Missing Values 5. Handling Categorical Data 5.0. Introduction 5.1. Encoding Nominal Categorical Features 5.2. Encoding Ordinal Categorical Features 5.3. Encoding Dictionaries of Features 5.4. Imputing Missing Class Values 5.5. Handling Imbalanced Classes 6. Handling Text 6.0. Introduction 6.1. Cleaning Text 6.2. Parsing and Cleaning HTML 6.3. Removing Punctuation 6.4. Tokenizing Text 6.5. Removing Stop Words 6.6. Stemming Words 6.7. Tagging Parts of Speech 6.8. Performing Named-Entity Recognition 6.9. Encoding Text as a Bag of Words 6.10. Weighting Word Importance 6.11. Using Text Vectors to Calculate Text Similarity in a Search Query 6.12. Using a Sentiment Analysis Classifier 7. Handling Dates and Times 7.0. Introduction 7.1. Converting Strings to Dates 7.2. Handling Time Zones 7.3. Selecting Dates and Times 7.4. Breaking Up Date Data into Multiple Features 7.5. Calculating the Difference Between Dates 7.6. Encoding Days of the Week 7.7. Creating a Lagged Feature 7.8. Using Rolling Time Windows 7.9. Handling Missing Data in Time Series 8. Handling Images 8.0. Introduction 8.1. Loading Images 8.2. Saving Images 8.3. Resizing Images 8.4. Cropping Images 8.5. Blurring Images 8.6. Sharpening Images 8.7. Enhancing Contrast 8.8. Isolating Colors 8.9. Binarizing Images 8.10. Removing Backgrounds 8.11. Detecting Edges 8.12. Detecting Corners 8.13. Creating Features for Machine Learning 8.14. Encoding Convolutions as a Feature 8.15. Encoding Color Histograms as Features 8.16. Using Pretrained Embeddings as a Feature 8.17. Detecting Objects with OpenCV 8.18. Classifying Images with Pytorch 9. Dimensionality Reduction Using Feature Extraction 9.0. Introduction 9.1. Reducing Features Using Principal Components 9.2. Reducing Features When Data Is Linearly Inseparable 9.3. Reducing Features by Maximizing Class Separability 9.4. Reducing Features Using Matrix Factorization 9.5. Reducing Features on Sparse Data 10. Dimensionality Reduction Using Feature Selection 10.0. Introduction 10.1. Thresholding Numerical Feature Variance 10.2. Thresholding Binary Feature Variance 10.3. Handling Highly Correlated Features 10.4. Removing Irrelevant Features for Classification 10.5. Recursively Eliminating Features 11. Model Evaluation 11.0. Introduction 11.1. Cross-Validating Models 11.2. Creating a Baseline Regression Model 11.3. Creating a Baseline Classification Model 11.4. Evaluating Binary Classifier Predictions 11.5. Evaluating Binary Classifier Thresholds 11.6. Evaluating Multiclass Classifier Predictions 11.7. Visualizing a Classifier’s Performance 11.8. Evaluating Regression Models 11.9. Evaluating Clustering Models 11.10. Creating a Custom Evaluation Metric 11.11. Visualizing the Effect of Training Set Size 11.12. Creating a Text Report of Evaluation Metrics 11.13. Visualizing the Effect of Hyperparameter Values 12. Model Selection 12.0. Introduction 12.1. Selecting Best Models Using Exhaustive Search 12.2. Selecting Best Models Using Randomized Search 12.3. Selecting Best Models from Multiple Learning Algorithms 12.4. Selecting Best Models When Preprocessing 12.5. Speeding Up Model Selection with Parallelization 12.6. Speeding Up Model Selection Using Algorithm-Specific Methods 12.7. Evaluating Performance After Model Selection About the Authors 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 arraysWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sourcesHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupporting vector machines (SVM), naäve Bayes, clustering, and tree-based modelsSaving, loading, and serving trained models from multiple frameworks 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