Machine Learning in Python: Essential Techniques for Predictive Analysis

Book Description

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.

Table of Contents

Chapter 1 The Two Essential Algorithms for Making Predictions
Chapter 2 Understand the Problem by Understanding the Data
Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data
Chapter 4 Penalized Linear Regression
Chapter 5 Building Predictive Models Using Penalized Linear Methods
Chapter 6 Ensemble Methods
Chapter 7 Building Ensemble Models with Python

中文：

书名：Python中的机器学习：预测分析的基本技术

了解使用Python分析数据和预测结果的更简单、更有效的方法

Python中的机器学习向您展示了如何仅使用两种核心机器学习算法成功地分析数据，以及如何使用Python应用它们。通过关注两个有效预测结果的算法系列，这本书能够提供工作机制的完整描述，并提供用特定的、可黑客攻击的代码说明该机制的示例。这些算法以简单的术语解释，不需要复杂的数学计算，并使用Python进行应用，并在实践中指导算法选择、数据准备和使用训练后的模型。您将学习一组核心的Python编程技术、构建预测模型的各种方法，以及如何测量每个模型的性能以确保使用正确的模型。关于惩罚线性回归和集成方法的章节深入研究了每种算法，您可以使用书中的样例代码来开发您自己的数据分析解决方案。

机器学习算法是数据分析和可视化的核心。在过去，这些方法需要在数学和统计学方面有很深的背景，通常还需要结合专门的R编程语言。这本书演示了如何使用更广泛使用和可访问的Python编程语言来实现机器学习。*使用线性和集成算法系列预测结果*构建可解决一系列简单和复杂问题的预测模型*使用Python应用核心机器学习算法*直接使用示例代码构建自定义解决方案

机器学习并不一定要复杂和高度专业化。通过使用更简单、有效和经过良好测试的方法，Python使这项技术更容易为更广泛的受众所接受。Python中的机器学习向您展示了如何做到这一点，而不需要广泛的数学或统计学背景。

Table of Contents

第一章预测的两种基本算法
第二章通过理解数据来理解问题
第3章预测模型构建：平衡性能、复杂性和大数据
Chapter 4 Penalized Linear Regression
第五章利用惩罚线性方法建立预测模型
Chapter 6 Ensemble Methods
第7章使用Python构建系综模型