
Sách Machine Learning with Python Cookbook Practical Solutions from Preprocessing to Deep Learning (Sách keo gáy, bìa mềm)
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 all the way 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 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.
You'll 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, and dates and times
Dimensionality reduction using feature extraction or feature 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
Saving and loading trained models from multiple frameworks
Categories:Computers - Artificial Intelligence (AI)
Year:2023
Edition:2 / converted
Language:english
Pages:416
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