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

Chris Albon

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商品描述

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.

Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

You’ll find recipes for:

  • Vectors, matrices, and arrays
  • 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), naïve Bayes, clustering, and neural networks
  • Saving and loading trained models

商品描述(中文翻譯)

這本實用指南提供了近200個獨立的配方,幫助您解決在日常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,您將能夠解決特定的問題,例如加載數據、處理文本或數值數據、模型選擇和降維等等。

每個配方都包含可以複製並粘貼到玩具數據集中以確保其正常運作的代碼。然後,您可以插入、結合或適應該代碼,以幫助構建您的應用程序。配方還包括一個討論,解釋解決方案並提供有意義的上下文。這本食譜書通過提供構建工作機器學習應用所需的實際操作,將您帶入理論和概念之外。

您將找到以下配方:

- 向量、矩陣和數組
- 處理數值和分類數據、文本、圖像和日期時間
- 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、以及k最近鄰算法
- 支持向量機(SVM)、朴素貝葉斯、聚類和神經網絡
- 保存和加載訓練好的模型