Python机器学习(第2版影印版)(英文版)

机器学习正在蚕食软件世界。在这本 Sebastian Raschka的畅销书《Python机器学习（ 第二版）》中，你将了解并学习到机器学习、神经 网络和深度学习的最前沿知识。 塞巴斯蒂安·拉施卡、瓦希德·麦加利利著的 《Python机器学习》更新并扩展了包括scikit- learn、Keras、TensorFlow在内的最新开源技术。 书中提供了使用Python创建有效的机器学习和深度 学习应用所需的实用知识和技术。 在涉及数据分析的高级主题之前，Sebastian Raschka和Vahid Mirjalili以其独特见解和专业知 识为你介绍机器学习和深度学习算法。本书将机器 学习的理论原理与实际编码方法相结合，以求全面 掌握机器学习理论及其Python实现。

塞巴斯蒂安·拉施卡，是密歇根州立大学的博士生，他在计算生物学领域提出了几种新的计算方法，还被科技博客Analytics Vidhya评为GitHub上具影响力的数据科学家。他有一整年都使用Python进行编程的经验，同时还多次参加数据科学应用与机器学习领域的研讨会。正是因为Sebastian 在数据科学、机器学习以及Python等领域拥有丰富的演讲和写作经验，他才有动力完成此书的撰写，目的是帮助那些不具备机器学习背景的人设计出由数据驱动的解决方案。

Preface Chapter 1: Giving Computers the Ability_ to Learn from Data Building intelligent machines to transform data into knowledge The three different types of machine learning Making predictions about the future with supervised learning Classification for predicting class labels Regression for predicting continuous outcomes Solving interactive problems with reinforcement learning Discovering hidden structures with unsupervised learning Finding subgroups with clustering Dimensionality reduction for data compression Introduction to the basic terminology and notations A roadmap for building machine learning systems Preprocessing - getting data into shape Training and selecting a predictive model Evaluating models and predicting unseen data instances Using Python for machine learning Installing Python and packages from the Python Package Index Using the Anaconda Python distribution and package manager Packages for scientific computing, data science, and machine learning Summary Chapter 2: Training Simple Machine Learning Algorithms for Classification Artificial neurons - a brief glimpse into the early history of machine learning The formal definition of an artificial neuron The perceptron learning rule Implementing a perceptron learning algorithm in Python An object-oriented perceptron API Training a perceptron model on the Iris dataset Adaptive linear neurons and the convergence of learning Minimizing cost functions with gradient descent Implementing Adaline in Python Improving gradient descent through feature scaling Large-scale machine learning and stochastic gradient descent Summary Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn Choosing a classification algorithm First steps with scikit-learn - training a perceptron Modeling class probabilities via logistic regression Logistic regression intuition and conditional probabilities Learning the weights of the logistic cost function Converting an Adaline implementation into an algorithm for logistic regression Training a logistic regression model with scikit-learn Tackling overfitting via regularization Maximum margin classification with support vector machines Maximum margin intuition Dealing with a nonlinearly separable case using slack variables