Machine Learning Kochbuch

Python programmers will find nearly 200 valuable and self-contained guides to tasks in the field of Machine Learning in this cookbook, covering everything from data preprocessing to deep learning, as is typical in daily work.

Developers familiar with Python and its libraries, including Pandas and Scikit-Learn, will successfully tackle specific problems—such as loading data, handling text and numerical data, selecting models, reducing dimensionality, and much more.

Each recipe includes code that you can copy, paste into a small sample dataset for testing, and then modify to build your own applications. Additionally, all solutions are discussed, and important connections are made. This cookbook helps you make the transition from theory and concepts into practice. It provides the practical tools you need to develop functional Machine Learning applications.

In this cookbook, you will find recipes for:

• Vectors, matrices, and arrays
• Handling numerical and categorical data, text, images, as well as date and time
• Reducing dimensionality through feature extraction or feature selection
• Model evaluation and selection
• Linear and logistic regression, trees and forests, and k-nearest neighbors
• Support Vector Machine (SVM), naive Bayes, clustering, and neural networks
• Saving and loading trained models

Chris has leveraged the cookbook format of his book to provide not only a reference for experienced professionals but also an easily accessible series of small tutorials that beginners will appreciate. This book is a valuable resource, whether you want to refresh your knowledge before a Data Scientist interview or are looking for a concise yet thorough reference for your desk.