Machine Learning with Python Cookbook. 2nd Edition

Spis treści książki

• Preface
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly 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 Classifiers 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 Observations 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

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