Python机器学习 第2版 影印版
作者： （美）塞巴斯蒂安 拉施卡（SebastianRaschka），（美）瓦希德 麦加利利（VahidMirjalili）著
出版时间： 2018年版
内容简介
《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**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。
目录
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
……
