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。

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
Alternative implementations in scikit-learn
Solving nonlinear problems using a kernel SVM
Kernel methods for linearly inseparable data
Using the kernel trick to find separating hyperplanes in
high-dimensional space
Decision tree learning
Maximizing information gain - getting the most bang for your buck
Building a decision tree
Combining multiple decision trees via random forests
K-nearest neighbors - a lazy learning algorithm
Summary
Chapter 4: Building Good Training Sets - Data Preprocessing
Dealing with missing data
Identifying missing values in tabular data
Eliminating samples or features with missing values
Imputing missing values
Understanding the scikit-learn estimator API
……