Machine Learning in Python: Essential Techniques for Predictive Analysis

Michael Bowles

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商品描述

Learn a simpler and more effective way to analyze data and predict outcomes with Python

Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.

Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.

  • Predict outcomes using linear and ensemble algorithm families
  • Build predictive models that solve a range of simple and complex problems
  • Apply core machine learning algorithms using Python
  • Use sample code directly to build custom solutions

Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.

商品描述(中文翻譯)

學習使用Python進行數據分析和預測結果的更簡單和更有效的方法

《Python機器學習》向您展示如何僅使用兩個核心機器學習算法成功地進行數據分析,以及如何使用Python應用這些算法。通過專注於兩個能夠有效預測結果的算法家族,本書能夠提供工作機制的完整描述,並通過具體的可編程代碼示例來說明這些機制。這些算法以簡單的術語解釋,不涉及複雜的數學,並使用Python應用,並提供算法選擇、數據準備以及實際使用訓練模型的指導。您將學習一組核心的Python編程技巧,各種構建預測模型的方法,以及如何測量每個模型的性能,以確保使用正確的模型。關於懲罰線性回歸和集成方法的章節深入探討了每個算法,您可以使用書中的示例代碼開發自己的數據分析解決方案。

機器學習算法是數據分析和可視化的核心。過去,這些方法需要深厚的數學和統計背景,通常還需要使用專門的R編程語言。本書演示了如何使用更廣泛使用且易於接觸的Python編程語言實現機器學習。

- 使用線性和集成算法家族預測結果
- 構建解決各種簡單和複雜問題的預測模型
- 使用Python應用核心機器學習算法
- 直接使用示例代碼構建自定義解決方案

機器學習不必是復雜和高度專業化的。Python使得這項技術更容易被更廣泛的人群接觸,使用的方法更簡單、有效且經過良好測試。《Python機器學習》向您展示如何實現這一點,而不需要廣泛的數學或統計背景。