Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide offers over 200 self-contained recipes to help you solve machine learning challenges, with code included for copying, pasting, and running with a toy dataset. Recipes cover topics such as vectors, matrices, arrays, data handling, dimensionality reduction, model evaluation, and more, with discussions explaining the solutions and providing meaningful context.

Format: Paperback / softback
Length: 380 pages
Publication date: 11 August 2023
Publisher: O'Reilly Media

This comprehensive guide offers over 200 self-contained recipes designed to assist you in overcoming machine learning challenges that you may encounter in your work. Whether you have a solid grasp of Python and its libraries, such as pandas and scikit-learn, you will be able to tackle diverse problems, ranging from data loading to model training and leveraging neural networks. Each recipe in this revised edition comes with code that you can readily copy, paste, and execute with a toy dataset to verify its functionality. From there, you can customize these recipes to suit your specific use case or application. Accompanying each recipe is a discussion that elucidates the solution and provides valuable context. By delving beyond theoretical concepts and gaining a firm understanding of the practical aspects required to build functional machine learning applications, you will discover recipes for various topics. These include working with vectors, matrices, and arrays, handling data from various sources, including CSV, JSON, SQL, databases, cloud storage, and more, managing numerical and categorical data, text, images, and dates and times, dimensionality reduction through feature extraction or 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, as well as saving and loading trained models from multiple frameworks. By following these step-by-step recipes, you will gain the skills and knowledge necessary to build robust and effective machine learning solutions that can address real-world problems and drive innovation in your field.

Weight: 718g
Dimension: 178 x 236 x 26 (mm)
ISBN-13: 9781098135720
Edition number: 2 Revised edition