Machine Learning with Python Cookbook, 2E

This practical guide offers over 200 standalone recipes to help you tackle challenges in machine learning that you may encounter in your work. If you are familiar with Python and its libraries, including pandas and scikit-learn, you can address specific problems ranging from data loading to training models and utilizing neural networks.

Each recipe in this updated edition includes code that you can copy, paste, and run with a test dataset to ensure it works. From there, you can adapt these recipes to your use case or application. The recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the fundamentals you need to create functional applications in the field of machine learning.

You will 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, as well as dates and times
• Dimensionality reduction through feature extraction or feature selection
• Model evaluation and selection
• Linear and logistic regression, trees and forests, as well as k-nearest neighbors
• Support vector machines (SVM), naive Bayes, clustering, and tree-based models
• Saving and loading trained models from multiple frameworks.