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出版社： 东南大学出版社

ISBN：9787564170776

商品编码：22298661412

包装：平装

出版时间：2017-04-01

基本信息

书名:Python机器学习(影印版)

定价：87.00元

作者:Sebastian,Raschka

出版社：东南大学出版社

出版日期：2017-04-01

ISBN：9787564170776

字数：

页码：425

版次：1

装帧：平装

开本：16开

商品重量：0.4kg

内容提要

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。

目录

Preface

Chapter 1： GiviComputers the Ability to Learfrom Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
Making predictions about the future with supervised learning
Classificatiofor predicting class labels
Regressiofor predicting continuous outes
Solving interactive problems with reinforcement learning
Discovering hiddestructures with unsupervised learning
Finding subgroups with clustering
Dimensionality reductiofor data pression
Aintroductioto 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 unseedata instances
Using Pythofor machine learning
Installing Pythopackages
Summary

Chapter 2： Training Machine Learning Algorithms
for Classification
Artificial neurons-a brief glimpse into the early history
of machine learning
Implementing a perceptrolearning algorithm iPython
Training a perceptromodel othe Iris dataset
Adaptive linear neurons and the convergence of learning
Minimizing cost functions with gradient descent
Implementing aAdaptive Linear NeuroiPython
Large scale machine learning and stochastic gradient descent
Summary

Chapter 3： A Tour of Machine Learning Classifiers Using
Scikit-learn
Choosing a classificatioalgorithm
First steps with scikit-learn
Training a perceptrovia scikit-learn
Modeling class probabilities via logistic regression
Logistic regressiointuitioand conditional probabilities
Learning the weights of the logistic cost function
Training a logistic regressiomodel with scikit-learn
Tackling overfitting via regularization
Maximum margiclassificatiowith support vector machines
Maximum margiintuition
Dealing with the nonlinearly separablecase using slack variables
Alternative implementations iscikit-learn
Solving nonlinear problems using a kernel SMM
Using the kernel trick to find separating hyperplanes ihigher
dimensional space
Decisiotree learning
Maximizing informatiogain-getting the most bang for the buck
Building a decisiotree
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-learestimator API
Handling categorical data
Mapping ordinal features
Encoding class labels
Performing one-hot encoding onominal features
Partitioning a dataset itraining and test sets
Bringing features onto the same scale
Selecting meaningful features
Sparse solutions with L1 regularization
Sequential feature selectioalgorithms
Assessing feature importance with random forests
Summary

Chapter 5： Com~ Data via Di~ Reduction
Unsupervised dimensionality reductiovia principal
ponent analysis
Total and explained variance
Feature transformation
Principal ponent analysis iscikit-learn
Supervised data pressiovia 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 ponent analysis for nonlinear mappings
Kernel functions and the kernel trick
Implementing a kernel principal ponent analysis iPython
Example 1-separating half-mooshapes
Example 2-separating concentric circles
Projecting new data points
Kernel principal ponent analysis iscikit-learn
Summary

Chapter 6： Learning Best Practices for Model Evaluation
and Hyperparameter Tuni~
Streamlining workflows with pipelines
Loading the Breast Cancer Wisconsidataset
Combining transformers and estimators ia pipeline
Using k-fold cross-validatioto assess model performance
The holdout method
K-fold cross-validation
Debugging algorithms with learning and validatiocurves
Diagnosing bias and variance problems with learning curves
Addressing overfitting and underfitting with validatiocurves
Fine-tuning machine learning models via grid search
Tuning hyperparameters via grid search
Algorithm selectiowith nested cross-validation
Looking at different performance evaluatiometrics
Reading a confusiomatrix
Optimizing the precisioand recall of a classificatiomodel
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 classificatiowith majority vote
Evaluating and tuning the ensemble classifier
Bagging-building aensemble 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 regressiomodel 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-learestimators
Setting up a SQLite database for data storage
Developing a web applicatiowith Flask
Our first Flask web application
Form validatioand rendering
Turning the movie classifier into a web application
Deploying the web applicatioto a public sewer
Updating the movie review classifier
Summary

Chapter 10： Predicting Continuous Target Variables
with R_Re_gressioAnalysis
Introducing a simple linear regressiomodel
Exploring the Housing Dataset
Visualizing the important characteristics of a dataset
Implementing aordinary least squares linear regressiomodel
Solving regressiofor regressioparameters with gradient descent
Estimating the coefficient of a regressiomodel via scikit-learn
Fitting a robust regressiomodel using RANSAC
Evaluating the performance of linear regressiomodels
Using regularized methods for regression
Turning a linear regressiomodel into a curve-polynomial regression
Modeling nonlinear relationships ithe Housing Dataset
Dealing with nonlinear relationships using random forests
Decisiotree 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 oa 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 plex functions with artificial neural works
Single-layer neural work recap
Introducing the multi-layer neural work architecture
Activating a neural work via forward propagation
Classifying handwrittedigits
Obtaining the MNIST dataset
Implementing a multi-layer perceptron
Training aartificial neural work
Computing the logistic cost function
Training neural works via backpropagation
Developing your intuitiofor backpropagation
Debugging neural works with gradient checking
Convergence ineural works
Other neural work architectures
Convolutional Neural Networks
Recurrent Neural Networks
A few last words about neural work implementation
Summary

Chapter 13： Parallelizing Neural Network Training with Theano
Building， piling， and running expressions with Theano
What is Theano
First steps with Theano
Configuring Theano
Working with array structures
Wrapping things up-a linear regressioexample
Choosing activatiofunctions for feedforward neural works
Logistic functiorecap
Estimating probabilities imulti-class classificatiovia the
softmax function
Broadening the output spectrum by using a hyperbolic tangent
Training neural works efficiently using Keras

Summary
Index

作者介绍

SebastiaRaschka，a PhD student at MichigaState University, who develops new putational methods ithe field of putational biology. He has beeranked as the number one most influential data scientist oGitHub by Analytics Vidhya. He has a yearlong experience iPythoprogramming and he has conducted several seminars othe practical applications of data science and machine learning. Talking and writing about data science, machine learning, and Pythoreally motivated Sebastiato write this book iorder to help people develop data-drivesolutions without necessarily needing to have a machine learning background. He has also actively contributed to opesource projects and methods that he implemented, which are now successfully used imachine learning petitions, such as Kaggle. Ihis free time, he works omodels for sports predictions, and if he is not ifront of the puter, he enjoys playing sports.

序言

Preface

Chapter 1： GiviComputers the Ability to Learfrom Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
Making predictions about the future with supervised learning
Classificatiofor predicting class labels
Regressiofor predicting continuous outes
Solving interactive problems with reinforcement learning
Discovering hiddestructures with unsupervised learning
Finding subgroups with clustering
Dimensionality reductiofor data pression
Aintroductioto 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 unseedata instances
Using Pythofor machine learning
Installing Pythopackages
Summary

Chapter 2： Training Machine Learning Algorithms
for Classification
Artificial neurons-a brief glimpse into the early history
of machine learning
Implementing a perceptrolearning algorithm iPython
Training a perceptromodel othe Iris dataset
Adaptive linear neurons and the convergence of learning
Minimizing cost functions with gradient descent
Implementing aAdaptive Linear NeuroiPython
Large scale machine learning and stochastic gradient descent
Summary

Chapter 3： A Tour of Machine Learning Classifiers Using
Scikit-learn
Choosing a classificatioalgorithm
First steps with scikit-learn
Training a perceptrovia scikit-learn
Modeling class probabilities via logistic regression
Logistic regressiointuitioand conditional probabilities
Learning the weights of the logistic cost function
Training a logistic regressiomodel with scikit-learn
Tackling overfitting via regularization
Maximum margiclassificatiowith support vector machines
Maximum margiintuition
Dealing with the nonlinearly separablecase using slack variables
Alternative implementations iscikit-learn
Solving nonlinear problems using a kernel SMM
Using the kernel trick to find separating hyperplanes ihigher
dimensional space
Decisiotree learning
Maximizing informatiogain-getting the most bang for the buck
Building a decisiotree
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-learestimator API
Handling categorical data
Mapping ordinal features
Encoding class labels
Performing one-hot encoding onominal features
Partitioning a dataset itraining and test sets
Bringing features onto the same scale
Selecting meaningful features
Sparse solutions with L1 regularization
Sequential feature selectioalgorithms
Assessing feature importance with random forests
Summary

Chapter 5： Com~ Data via Di~ Reduction
Unsupervised dimensionality reductiovia principal
ponent analysis
Total and explained variance
Feature transformation
Principal ponent analysis iscikit-learn
Supervised data pressiovia 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 ponent analysis for nonlinear mappings
Kernel functions and the kernel trick
Implementing a kernel principal ponent analysis iPython
Example 1-separating half-mooshapes
Example 2-separating concentric circles
Projecting new data points
Kernel principal ponent analysis iscikit-learn
Summary

Chapter 6： Learning Best Practices for Model Evaluation
and Hyperparameter Tuni~
Streamlining workflows with pipelines
Loading the Breast Cancer Wisconsidataset
Combining transformers and estimators ia pipeline
Using k-fold cross-validatioto assess model performance
The holdout method
K-fold cross-validation
Debugging algorithms with learning and validatiocurves
Diagnosing bias and variance problems with learning curves
Addressing overfitting and underfitting with validatiocurves
Fine-tuning machine learning models via grid search
Tuning hyperparameters via grid search
Algorithm selectiowith nested cross-validation
Looking at different performance evaluatiometrics
Reading a confusiomatrix
Optimizing the precisioand recall of a classificatiomodel
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 classificatiowith majority vote
Evaluating and tuning the ensemble classifier
Bagging-building aensemble 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 regressiomodel 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-learestimators
Setting up a SQLite database for data storage
Developing a web applicatiowith Flask
Our first Flask web application
Form validatioand rendering
Turning the movie classifier into a web application
Deploying the web applicatioto a public sewer
Updating the movie review classifier
Summary

Chapter 10： Predicting Continuous Target Variables
with R_Re_gressioAnalysis
Introducing a simple linear regressiomodel
Exploring the Housing Dataset
Visualizing the important characteristics of a dataset
Implementing aordinary least squares linear regressiomodel
Solving regressiofor regressioparameters with gradient descent
Estimating the coefficient of a regressiomodel via scikit-learn
Fitting a robust regressiomodel using RANSAC
Evaluating the performance of linear regressiomodels
Using regularized methods for regression
Turning a linear regressiomodel into a curve-polynomial regression
Modeling nonlinear relationships ithe Housing Dataset
Dealing with nonlinear relationships using random forests
Decisiotree 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 oa 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 plex functions with artificial neural works
Single-layer neural work recap
Introducing the multi-layer neural work architecture
Activating a neural work via forward propagation
Classifying handwrittedigits
Obtaining the MNIST dataset
Implementing a multi-layer perceptron
Training aartificial neural work
Computing the logistic cost function
Training neural works via backpropagation
Developing your intuitiofor backpropagation
Debugging neural works with gradient checking
Convergence ineural works
Other neural work architectures
Convolutional Neural Networks
Recurrent Neural Networks
A few last words about neural work implementation
Summary

Chapter 13： Parallelizing Neural Network Training with Theano
Building， piling， and running expressions with Theano
What is Theano
First steps with Theano
Configuring Theano
Working with array structures
Wrapping things up-a linear regressioexample
Choosing activatiofunctions for feedforward neural works
Logistic functiorecap
Estimating probabilities imulti-class classificatiovia the
softmax function
Broadening the output spectrum by using a hyperbolic tangent
Training neural works efficiently using Keras

Summary
Index

深入探索Python在机器学习领域的强大力量 机器学习，作为人工智能的核心驱动力，正以前所未有的速度渗透到我们生活的方方面面，从智能推荐系统到自动驾驶，再到医疗诊断和金融风控，其应用前景无垠。而Python，凭借其简洁的语法、丰富的库生态和庞大的社区支持，已然成为机器学习领域最受青睐的编程语言。本书正是为那些渴望掌握Python机器学习精髓，或是希望在这一激动人心的领域开拓进取的读者量身打造。 我们并非仅仅罗列枯燥的算法定义和晦涩的数学公式，而是力求以一种更具实践导向、更富启发性的方式，引领您一步步走进Python机器学习的精彩世界。本书将从最基础的概念讲起，逐步深入到复杂的模型和前沿的技术，确保您在掌握理论知识的同时，也能拥有解决实际问题的能力。 核心内容概览： 第一部分：机器学习的基石——理解核心概念与准备工作 在正式踏上机器学习的旅程之前，充分的准备和对基本概念的深刻理解至关重要。本部分将为您打下坚实的基础，让您能够更自信地迎接后续的学习挑战。 Python基础与环境搭建： 对于初次接触Python的读者，我们将快速回顾Python的核心语法，包括数据类型、控制流、函数以及面向对象编程的基本概念。更重要的是，我们将详细指导您如何在您的操作系统上安装和配置Python开发环境，包括常用的集成开发环境（IDE）如PyCharm、VS Code，以及包管理工具pip。我们将重点介绍如何安装和管理至关重要的机器学习库，如NumPy、Pandas和Matplotlib，它们将是您进行数据处理、科学计算和可视化分析的得力助手。 数据科学的利器：NumPy与Pandas： 在机器学习项目中，数据是原材料。NumPy为Python提供了强大的多维数组对象（ndarray），使其能够高效地进行数值计算，这是许多机器学习算法的基础。我们将深入讲解NumPy的数组创建、索引、切片、数学运算以及广播机制。Pandas库则提供了更加灵活和强大的数据结构——Series和DataFrame，它们非常适合处理表格型数据，如CSV文件、数据库记录等。您将学习如何使用Pandas进行数据的读取、清洗、转换、合并、分组以及缺失值处理，为后续的模型训练做好准备。 数据可视化：Matplotlib与Seaborn： 一图胜千言。有效的数据可视化能够帮助我们更好地理解数据分布、特征之间的关系以及模型训练的效果。Matplotlib是Python中最基础但功能强大的绘图库，我们将学习如何绘制折线图、散点图、柱状图、直方图等。在此基础上，Seaborn库提供了更高级、更美观的统计图形绘制功能，能够轻松生成复杂的探索性数据分析图表，如热力图、箱线图、小提琴图等，帮助您洞察数据中的隐藏模式。 第二部分：监督学习——从基础到进阶的经典模型 监督学习是机器学习中最常见也是应用最广泛的一种类型，其目标是根据带有标签的训练数据来预测未知数据的标签。本部分将系统地介绍一系列经典的监督学习算法。 线性回归与逻辑回归： 线性回归是预测连续值变量的最简单也是最基础的模型，常用于分析变量之间的线性关系。我们将讲解线性回归的原理、损失函数（如均方误差）以及模型训练的优化方法（如梯度下降）。逻辑回归虽然名字带有“回归”，但它实际上是一个强大的分类算法，常用于二分类问题。我们将深入理解其Sigmoid函数、交叉熵损失函数以及如何将其推广到多分类问题。 支持向量机（SVM）： 支持向量机是一种强大且灵活的分类模型，它通过找到最优的超平面来分隔不同类别的数据。我们将深入讲解SVM的核心思想，包括最大间隔、核函数（线性核、多项式核、高斯核RBF）以及软间隔的概念，理解SVM如何处理非线性可分的数据。 决策树与随机森林： 决策树是一种直观且易于理解的分类和回归模型，它通过一系列的“if-then”规则来做出预测。我们将讲解决策树的构建过程，包括信息增益、基尼不纯度等概念，以及如何避免过拟合。随机森林作为一种集成学习方法，通过构建多个决策树并集成它们的预测结果，能够显著提高模型的鲁棒性和准确性，是处理复杂数据集的强大武器。 K近邻（KNN）： K近邻算法是一种非参数的、基于实例的学习方法。它通过计算新样本与训练集中所有样本的距离，并根据“少数服从多数”的原则进行分类（或取平均值进行回归）。我们将讲解距离度量（欧氏距离、曼哈顿距离等）、K值的选择以及KNN算法的优缺点。 朴素贝叶斯： 朴素贝叶斯算法基于贝叶斯定理，并假设特征之间是相互独立的（“朴素”的假设）。尽管存在这种简化假设，朴素贝叶斯在文本分类等领域表现出色，且训练速度快。我们将讲解其概率模型和应用场景。 第三部分：无监督学习——挖掘数据中的隐藏结构 无监督学习则是在没有预设标签的情况下，让模型自行发现数据中的模式、结构和关系。本部分将介绍几种重要的无监督学习算法。 聚类算法：K-Means与层次聚类： 聚类是将相似的数据点分组到不同的簇中。K-Means是最经典和广泛使用的聚类算法之一，我们将深入理解其迭代过程、簇中心的选择以及如何评估聚类结果。层次聚类则能够构建数据点的层级结构，生成聚类树，提供不同粒度的聚类视图。 降维技术：主成分分析（PCA）与t-SNE： 在高维数据集中，常常存在冗余信息，导致模型训练效率低下或过拟合。降维技术能够将高维数据映射到低维空间，同时尽可能保留数据的重要信息。主成分分析（PCA）是一种线性的降维方法，通过找到数据方差最大的方向来提取主要成分。t-SNE（t-Distributed Stochastic Neighbor Embedding）则是一种强大的非线性降维技术，尤其擅长可视化高维数据，并能够保持局部数据的结构。 关联规则挖掘：Apriori算法： 关联规则挖掘用于发现数据项之间的有趣关系，常用于市场篮子分析，如“购买了牛奶的顾客也常常购买面包”。Apriori算法是经典的关联规则挖掘算法，我们将学习如何生成频繁项集和关联规则。 第四部分：模型评估与调优——提升模型性能的关键 仅仅构建模型是不够的，如何评估模型的性能并对其进行优化，是机器学习项目成功的关键。 模型评估指标： 对于分类问题，我们将学习准确率、精确率、召回率、F1分数、ROC曲线和AUC值等关键评估指标，理解它们各自的含义和适用场景。对于回归问题，我们将介绍均方误差（MSE）、均方根误差（RMSE）、平均绝对误差（MAE）以及R²分数等。 交叉验证： 为了更可靠地评估模型的泛化能力，避免过拟合，交叉验证是一种必不可少的技术。我们将讲解K折交叉验证、留一法交叉验证等常用方法。 超参数调优：网格搜索与随机搜索： 大多数机器学习模型都存在超参数，需要手动调整才能达到最优性能。网格搜索会穷举所有可能的参数组合，而随机搜索则以随机的方式搜索参数空间，两者都是有效的调优策略。 正则化技术：L1与L2正则化： 正则化是一种防止模型过拟合的有效手段，它通过在损失函数中添加惩罚项来约束模型的复杂度。我们将深入理解L1（Lasso）和L2（Ridge）正则化的原理及其对模型参数的影响。 第五部分：深度学习入门——探索神经网络的奥秘 随着人工智能的飞速发展，深度学习成为了机器学习领域的热门话题。本部分将为您开启深度学习的大门。 神经网络基础： 我们将从最基本的感知机开始，逐步介绍多层感知机（MLP）、激活函数（ReLU、Sigmoid、Tanh）、前向传播与反向传播算法，以及损失函数和优化器。 TensorFlow与Keras： TensorFlow是Google开发的一款强大的深度学习框架，而Keras则是一个用户友好的高级API，它能够运行在TensorFlow之上，极大地简化了深度学习模型的构建和训练过程。我们将指导您使用Keras构建和训练各种类型的神经网络，包括卷积神经网络（CNN）和循环神经网络（RNN）。 本书的特色： 理论与实践并重： 我们不仅深入浅出地讲解机器学习的理论概念，更注重通过丰富的Python代码示例来演示如何将这些理论应用于实际问题。 循序渐进的学习路径： 从基础概念到高级模型，本书的设计遵循逻辑清晰、难度递增的学习路径，让您能够逐步建立对机器学习的全面认识。 丰富的数据集与项目实战： 书中将提供多种真实世界的数据集，并引导您完成一系列具有代表性的机器学习项目，让您在实战中巩固所学知识。 前沿技术探讨： 在对经典模型进行深入讲解的同时，本书也将适时引入深度学习等前沿技术，帮助您了解机器学习的最新发展趋势。 无论您是希望入门机器学习的初学者，还是希望提升技能的开发者，抑或是对人工智能充满好奇的探索者，本书都将是您不可或缺的学习伴侣。让我们一起，用Python驱动智能，探索机器学习的无限可能！

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我是一名在校的计算机专业学生，一直对数据科学和人工智能领域很感兴趣，尤其是机器学习。在选择学习资料时，我倾向于一些能够深入理解理论基础，并且能够快速上手实践的书籍。《Python机器学习（影印版）》就完美契合了我的需求。它在理论深度上做得相当不错，对于像线性回归、逻辑回归、决策树、支持向量机（SVM）、神经网络等经典算法，都有非常详尽的讲解，不仅介绍了算法的思想，还深入剖析了其背后的数学原理和优缺点。更重要的是，这本书没有停留在理论层面，而是提供了大量的Python代码实现，并且使用了scikit-learn等主流库，这对于我们这些需要将理论应用于实际项目学习的学生来说，是极其宝贵的。书中的案例分析也很有针对性，覆盖了分类、回归、聚类等多种机器学习任务，让我能够通过实际操作来巩固理论知识。读完这本书，我感觉自己在理解机器学习算法的本质以及如何运用Python进行实现方面，都有了质的飞跃。

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这本书让我对机器学习的理解不再是碎片化的。之前我零散地接触过一些机器学习的算法，但总觉得像是东拼西凑，缺乏一个完整的体系。《Python机器学习（影印版）》恰好填补了我的这个空白。它以清晰的脉络，从机器学习的“是什么”、“为什么”到“怎么做”，层层递进，将复杂的知识点梳理得井井有条。我尤其欣赏它在介绍各种模型时，不仅仅是给出代码，而是花了很大篇幅解释算法的原理、假设以及适用场景，这有助于我理解在不同问题下应该选择哪种模型。书中提供的代码示例非常高质量，不仅仅是功能实现，还体现了良好的编程风格和实践经验，这对我学习如何写出可维护、可扩展的代码非常有帮助。此外，对于模型的评估和选择，书中也给出了非常实用的建议和方法，这让我摆脱了“盲目尝试”的困境，能够更科学地进行模型开发。

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这本书简直是新手入门的福音！我之前对机器学习完全是零基础，看着各种高大上的概念就头疼。但是，《Python机器学习（影印版）》的讲解方式真的非常亲民。作者从最基础的Python环境搭建讲起，一步步引导读者熟悉常用的库，比如NumPy、Pandas和Matplotlib，这些都是后续学习机器学习的基石，讲得非常扎实。而且，书中对于每一个算法的讲解，都配有清晰的数学公式推导，但同时又不会让人生畏，它会用非常直观的比喻和图示来解释这些公式背后的逻辑，让人一下子就能理解。我尤其喜欢它在讲解监督学习和无监督学习时，举的那些贴近生活的例子，比如垃圾邮件分类、客户细分等等，让我觉得机器学习离我并不遥远，而是可以解决实际问题的工具。书中的代码示例也非常完整，我可以直接复制运行，并且有详细的注释，即使我不完全理解每一个细节，也能跟着书的节奏走。读完前面的章节，我感觉自己对机器学习的整体框架有了一个初步的认识，不再是那个对“黑箱”感到恐惧的小白了。

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作为一名有一定Python基础但对机器学习感到迷茫的开发者，我一直在寻找一本能够系统性地梳理机器学习知识体系并提供实用指导的书。这本书的结构安排真的非常合理。它从机器学习的基本概念、数据预处理、特征工程开始，然后逐步深入到各种主流的算法模型，包括监督学习、无监督学习、半监督学习，甚至还涉及了深度学习的入门。最让我满意的是，书中并没有回避复杂的数学概念，而是以一种循序渐进的方式介绍，并辅以大量的图示和代码示例，这让原本抽象的理论变得生动易懂。每介绍完一个算法，都会有相应的代码实现，而且这些代码都经过优化，可以直接用于实际项目。我还特别喜欢书中关于模型评估和调优的部分，这对于避免“过拟合”和“欠拟合”等常见问题至关重要，也让我学到了很多实用的技巧。读完这本书，我感觉自己不仅掌握了理论知识，还具备了独立解决实际机器学习问题的能力。

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我是一名对数据分析有浓厚兴趣的职场人士，一直想将机器学习技术应用到我的工作中，但苦于没有合适的入门教材。《Python机器学习（影印版）》真的给了我很大的惊喜。它以非常友好和易于理解的方式，讲解了机器学习的核心概念和常用算法。书中的例子非常贴合实际应用场景，比如如何进行数据清洗、特征提取，以及如何构建一个预测模型。我尤其喜欢它对scikit-learn库的讲解，这是我之前接触较少但非常重要的一个库，通过这本书，我能够快速掌握它的基本用法，并将其应用到我的数据分析项目中。书中对于模型评估和参数调优的介绍也让我受益匪浅，让我能够更有效地衡量模型的性能，并找到最佳的模型配置。总的来说，这本书为我打开了机器学习应用的大门，让我能够更自信地运用这项技术来解决实际业务问题。