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《Python机器学习：第2版（影印版）》带你进入预测分析的世界，通过演示告诉你为什么Python是世界很好不错的数据科学语言之一。书中涵盖了包括scikit-learn、Theano和Keras在内的大量功能强大的Python库、操作指南以及从情感分析到神经网络的各色小技巧，很快你就能够解答你个人及组织所面对的那些很重要的问题。

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 Inde 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 Artifi neurons-a brief glimpse into the early history of machine learning The formal definition of an artifi 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 Maimum margin classification with support vector machines Maimum margin intuition Dealing with a nonlinearly separable case using slack variables ……

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。

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