Previous ed.: / Chris Albon. 2018

Working with vectors, matrices, and arrays in NumPy -- Loading data -- Data wrangling -- Handling numerical data -- Handling categorical data -- Handling text --Handling dates and times -- Handling images --Dimensionality reduction using feature extraction -- Dimensionality reduction using feature selection -- Model evaluation -- Model selection -- Linear regression -- Trees and forests -- K-Nearest neigbors -- Logistic regression -- Support vector machines -- Naive Bayes -- Clustering -- Tenors with PyTorch -- Neural networks -- Neural networks for unstructured data -- Saving, loading, and serving trained models

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

Includes index