Machine Learning with Python Cookbook, 2nd Edition

Machine Learning with Python Cookbook, 2nd Edition
by Kyle Gallatin, Chris Albon
Released October 2023
Publisher Finelybook 出版社： O'Reilly Media, Inc.
ISBN: 9781098135669

Book Description
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
Supporting vector machines (SVM), naive Bayes, clustering, and tree-based models
Saving and loading trained models from multiple frameworks

1. Working with Vectors, Matrices and Arrays in NumPy
2. Loading Data
3. Data Wrangling
4. Handling Numerical Data
5. Handling Categorical Data
6. Handling Text
6.0. Introduction
6.1. Cleaning Text
6.2. Parsing and Cleaning HTML
6.3. Removing Punctuation
6.4. Tokenizing Text
6.5. Removing Stop Words
6.6. Stemming Words
6.7. Tagging Parts of Speech
6.8. Performing Named-Entity Recognition
6.9. Encoding Text as a Bag of Words
6.10. Weighting Word Importance
6.11. Using Word Vectors to Calculate Text Similarity in a Search Query 6.12. Using a Sentiment Analysis Classifier