Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning 2nd Edition

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

這本實用指南提供了200多個獨立的配方，幫助您解決在工作中可能遇到的機器學習挑戰。如果您熟悉Python及其庫，包括pandas和scikit-learn，您將能夠從加載數據到訓練模型並利用神經網絡解決特定問題。

本更新版的每個配方都包含了可複製、粘貼和運行的代碼，以確保其正常運作。然後，您可以根據您的用例或應用程序調整這些配方。配方包括一個討論，解釋解決方案並提供有意義的上下文。通過學習機器學習應用所需的細節，超越理論和概念。

您將找到以下配方：
- 向量、矩陣和數組
- 使用CSV、JSON、SQL、數據庫、雲存儲和其他來源處理數據
- 處理數值和分類數據、文本、圖像、日期和時間
- 使用特徵提取或特徵選擇進行降維
- 模型評估和選擇
- 線性和邏輯回歸、樹和森林、k最近鄰算法
- 支持向量機（SVM）、朴素貝葉斯、聚類和基於樹的模型
- 從多個框架保存和加載訓練好的模型