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 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. 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 Preface Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments 1. Working with Vectors, Matrices, and Arrays in NumPy 1.0. Introduction 1.1. Creating a Vector 1.2. Creating a Matrix 1.3. Creating a Sparse Matrix 1.4. Preallocating 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 an Avro File 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 3. Data Wrangling 3.0. Introduction 3.1. Creating a Dataframe 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 Color Histograms as Features 8.15. Using Pretrained Embeddings as Features 8.16. Detecting Objects with OpenCV 8.17. 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 the Best Models Using Exhaustive Search 12.2. Selecting the Best Models Using Randomized Search 12.3. Selecting the Best Models from Multiple Learning Algorithms 12.4. Selecting the 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 13. Linear Regression 13.0. Introduction 13.1. Fitting a Line 13.2. Handling Interactive Effects 13.3. Fitting a Nonlinear Relationship 13.4. Reducing Variance with Regularization 13.5. Reducing Features with Lasso Regression 14. Trees and Forests 14.0. Introduction 14.1. Training a Decision Tree Classifier 14.2. Training a Decision Tree Regressor 14.3. Visualizing a Decision Tree Model 14.4. Training a Random Forest Classifier 14.5. Training a Random Forest Regressor 14.6. Evaluating Random Forests with Out-of-Bag Errors 14.7. Identifying Important Features in Random Forests 14.8. Selecting Important Features in Random Forests 14.9. Handling Imbalanced Classes 14.10. Controlling Tree Size 14.11. Improving Performance Through Boosting 14.12. Training an XGBoost Model 14.13. Improving Real-Time Performance with LightGBM 15. K-Nearest Neighbors 15.0. Introduction 15.1. Finding an Observation’s Nearest Neighbors 15.2. Creating a K-Nearest Neighbors Classifier 15.3. Identifying the Best Neighborhood Size 15.4. Creating a Radius-Based Nearest Neighbors Classifier 15.5. Finding Approximate Nearest Neighbors 15.6. Evaluating Approximate Nearest Neighbors 16. Logistic Regression 16.0. Introduction 16.1. Training a Binary Classifier 16.2. Training a Multiclass Classifier 16.3. Reducing Variance Through Regularization 16.4. Training a Classifier on Very Large Data 16.5. Handling Imbalanced Classes 17. Support Vector Machines 17.0. Introduction 17.1. Training a Linear Classifier 17.2. Handling Linearly Inseparable Classes Using Kernels 17.3. Creating Predicted Probabilities 17.4. Identifying Support Vectors 17.5. Handling Imbalanced Classes 18. Naive Bayes 18.0. Introduction 18.1. Training a Classifier for Continuous Features 18.2. Training a Classifier for Discrete and Count Features 18.3. Training a Naive Bayes Classifier for Binary Features 18.4. Calibrating Predicted Probabilities 19. Clustering 19.0. Introduction 19.1. Clustering Using K-Means 19.2. Speeding Up K-Means Clustering 19.3. Clustering Using Mean Shift 19.4. Clustering Using DBSCAN 19.5. Clustering Using Hierarchical Merging 20. Tensors with PyTorch 20.0. Introduction 20.1. Creating a Tensor 20.2. Creating a Tensor from NumPy 20.3. Creating a Sparse Tensor 20.4. Selecting Elements in a Tensor 20.5. Describing a Tensor 20.6. Applying Operations to Elements 20.7. Finding the Maximum and Minimum Values 20.8. Reshaping Tensors 20.9. Transposing a Tensor 20.10. Flattening a Tensor 20.11. Calculating Dot Products 20.12. Multiplying Tensors 21. Neural Networks 21.0. Introduction 21.1. Using Autograd with PyTorch 21.2. Preprocessing Data for Neural Networks 21.3. Designing a Neural Network 21.4. Training a Binary Classifier 21.5. Training a Multiclass Classifier 21.6. Training a Regressor 21.7. Making Predictions 21.8. Visualize Training History 21.9. Reducing Overfitting with Weight Regularization 21.10. Reducing Overfitting with Early Stopping 21.11. Reducing Overfitting with Dropout 21.12. Saving Model Training Progress 21.13. Tuning Neural Networks 21.14. Visualizing Neural Networks 22. Neural Networks for Unstructured Data 22.0. Introduction 22.1. Training a Neural Network for Image Classification 22.2. Training a Neural Network for Text Classification 22.3. Fine-Tuning a Pretrained Model for Image Classification 22.4. Fine-Tuning a Pretrained Model for Text Classification 23. Saving, Loading, and Serving Trained Models 23.0. Introduction 23.1. Saving and Loading a scikit-learn Model 23.2. Saving and Loading a TensorFlow Model 23.3. Saving and Loading a PyTorch Model 23.4. Serving scikit-learn Models 23.5. Serving TensorFlow Models 23.6. Serving PyTorch Models in Seldon Index 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