**Python机器学习（影印版） [Python Machine Learning]**这本书，是由东南大学出版社在2017-04-01月出版的，本书著作者是 **Sebastian，Raschka 著**，此次本版是第1次印刷发行， 国际标准书号(ISBN)：9787564170776，品牌为南京东南大学出版社, 这本书的包装是16开平装，所用纸张为胶版纸，全书共有425页字数55万3000字， 是一本非常不错的Python编程书籍。

## 此书内容摘要

机器学习和预测分析正在改变商业和其他组织的运作模式。

《**Python机器学习**（影印版）》将带你进入预测分析的世界，通过演示告诉你为什么Python是世界数据科学语言之一。如果你想询问更深入的数据问题，或是想增进、拓展机器学习系统的能力，这本实用的书籍可谓是无价之宝。

《Python机器学习（影印版）》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*重要的问题。

## 关于此书作者

Sebastian Raschka，a PhD student at Michigan State University, who develops new computational methods in the field of computational biology. He has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning. Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background. He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle. In his free time, he works on models for sports predictions, and if he is not in front of the computer, he enjoys playing sports.

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## Python机器学习（影印版） [Python Machine Learning]图书的目录

Preface

Chapter 1： Givin 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

An 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 packages

Summary

Chapter 2： Training Machine Learning Algorithms

for Classification

Artificial neurons-a brief glimpse into the early history

of machine learning

Implementing a perceptron learning algorithm in Python

Training a perceptron model on the Iris dataset

Adaptive linear neurons and the convergence of learning

Minimizing cost functions with gradient descent

Implementing an Adaptive Linear Neuron in Python

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 via scikit-learn

Modeling class probabilities via logistic regression

Logistic regression intuition and conditional probabilities

Learning the weights of the logistic cost function

Training a logistic regression model with scikit-learn

Tackling overfitting via regularization

Maximum margin classification with support vector machines

Maximum margin intuition

Dealing with the nonlinearly separablecase using slack variables

Alternative implementations in scikit-learn

Solving nonlinear problems using a kernel SMM

Using the kernel trick to find separating hyperplanes in higher

dimensional space

Decision tree learning

Maximizing information gain-getting the most bang for the buck

Building a decision tree

Combining weak to strong learners via random forests

K-nearest neighbors-a lazy learning algorithm

Summary

Chapter 4： Building Good Training Sets-Data Preprocessing

Dealing with missing data

Eliminating samples or features with missing values

Imputing missing values

Understanding the scikit-learn estimator API

Handling categorical data

Mapping ordinal features

Encoding class labels

Performing one-hot encoding on nominal features

Partitioning a dataset in training and test sets

Bringing features onto the same scale

Selecting meaningful features

Sparse solutions with L1 regularization

Sequential feature selection algorithms

Assessing feature importance with random forests

Summary

Chapter 5： Com~ Data via Di~ Reduction

Unsupervised dimensionality reduction via principal

component analysis

Total and explained variance

Feature transformation

Principal component analysis in scikit-learn

Supervised data compression via linear discriminant analysis

Computing the scatter matrices

Selecting linear discriminants for the new feature subspace

Projecting samples onto the new feature space

LDA via scikit-learn

Using kernel principal component analysis for nonlinear mappings

Kernel functions and the kernel trick

Implementing a kernel principal component analysis in Python

Example 1-separating half-moon shapes

Example 2-separating concentric circles

Projecting new data points

Kernel principal component analysis in scikit-learn

Summary

Chapter 6： Learning Best Practices for Model Evaluation

and Hyperparameter Tuni~

Streamlining workflows with pipelines

Loading the Breast Cancer Wisconsin dataset

Combining transformers and estimators in a pipeline

Using k-fold cross-validation to assess model performance

The holdout method

K-fold cross-validation

Debugging algorithms with learning and validation curves

Diagnosing bias and variance problems with learning curves

Addressing overfitting and underfitting with validation curves

Fine-tuning machine learning models via grid search

Tuning hyperparameters via grid search

Algorithm selection with nested cross-validation

Looking at different performance evaluation metrics

Reading a confusion matrix

Optimizing the precision and recall of a classification model

Plotting a receiver operating characteristic

The scoring metrics for multiclass classification

Summary

Chapter 7： Combining Different Models for Ensemble Learning

Learning with ensembles

Implementing a simple majority vote classifier

Combining different algorithms for classification with majority vote

Evaluating and tuning the ensemble classifier

Bagging-building an ensemble of classifiers from

bootstrap samples

Leveraging weak learners via adaptive boosting

Summary

Chapter 8： Applying Machine Learning to Sentiment Analysis

Obtaining the IMDb movie review dataset

Introducing the bag-of-words model

Transforming words into feature vectors

Assessing word relevancy via term frequency-inverse

document frequency

Cleaning text data

Processing documents into tokens

Training a logistic regression model for document classification

Working with bigger data-online algorithms and

out-of-core learning

Summary

Chapter 9： Embedding a Machine Learning Model into

a Web Application

Serializing fitted scikit-learn estimators

Setting up a SQLite database for data storage

Developing a web application with Flask

Our first Flask web application

Form validation and rendering

Turning the movie classifier into a web application

Deploying the web application to a public sewer

Updating the movie review classifier

Summary

Chapter 10： Predicting Continuous Target Variables

with R_Re_gression Analysis

Introducing a simple linear regression model

Exploring the Housing Dataset

Visualizing the important characteristics of a dataset

Implementing an ordinary least squares linear regression model

Solving regression for regression parameters with gradient descent

Estimating the coefficient of a regression model via scikit-learn

Fitting a robust regression model using RANSAC

Evaluating the performance of linear regression models

Using regularized methods for regression

Turning a linear regression model into a curve-polynomial regression

Modeling nonlinear relationships in the Housing Dataset

Dealing with nonlinear relationships using random forests

Decision tree regression

Random forest regression

Summary

Chapter 11： Working with Unlabeled Data- Cluste~

Grouping objects by similarity using k-means

K-means++

Hard versus soft clustering

Using the elbow method to find the optimal number of clusters

Quantifying the quality of clustering via silhouette plots

Organizing clusters as a hierarchical tree

Performing hierarchical clustering on a distance matrix

Attaching dendrograms to a heat map

Applying agglomerative clustering via scikit-learn

Locating regions of high density via DBSCAN

Summary

Chapter 12： Training Artificial Neural Networks for Image Recognition

Modeling complex functions with artificial neural networks

Single-layer neural network recap

Introducing the multi-layer neural network architecture

Activating a neural network via forward propagation

Classifying handwritten digits

Obtaining the MNIST dataset

Implementing a multi-layer perceptron

Training an artificial neural network

Computing the logistic cost function

Training neural networks via backpropagation

Developing your intuition for backpropagation

Debugging neural networks with gradient checking

Convergence in neural networks

Other neural network architectures

Convolutional Neural Networks

Recurrent Neural Networks

A few last words about neural network implementation

Summary

Chapter 13： Parallelizing Neural Network Training with Theano

Building， compiling， and running expressions with Theano

What is Theano?

First steps with Theano

Configuring Theano

Working with array structures

Wrapping things up-a linear regression example

Choosing activation functions for feedforward neural networks

Logistic function recap

Estimating probabilities in multi-class classification via the

softmax function

Broadening the output spectrum by using a hyperbolic tangent

Training neural networks efficiently using Keras

Summary

Index

## 部分内容试读

We live in the midst of a data deluge. According to recent estimates， 2.5 quintillion （10i8） bytes of data are generated on a daily basis. This is so much data that over 90 percent of the information that we store nowadays was generated in the past decade alone. Unfortunately， most of this information cannot be used by humans. Either the data is beyond the means of standard analytical methods， or it is simply too vast for our limited minds to even comprehend.

Through Machine Learning， we enable computers to process， learn from， and draw actionable insights out of the otherwise impenetrable walls of big data. From the massive supercomputers that support Google s search engines to the smart phones that we carry in our pockets， we rely on Machine Learning to power most of the world around us - often， without even knowing it.

As modem pioneers in the brave new world of big data， it then behooves us to learn more about Machine Learning. What is Machine Learning and how does it work? How can I use Machine Learning to take a glimpse into the unknown， power my business， or just find out what the Internet at large thinks about my favorite movie? All of this and more will be covered in the following chapters authored by my good friend and colleague， Sebastian Raschka. When away from taming my otherwise irascible pet dog， Sebashan has tirelessly devoted his free time to the open source Machine Learning community. Over the past several years， Sebastian has developed dozens of popular tutorials that cover topics in Machine Learning and data visualization in Python. He has also developed and contributed to several open source Python packages， several of which are now part of the core Python Machine Learning workflow.

Owing to his vast expertise in this field， I am confident that Sebashan's insights into the world of Machine Learning in Python will be invaluable to users of all experience levels. l wholeheartedly recommendy this book to anyone looking to gain a broader and more practical und Yerstanding of Machine Learning.

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