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Sebastian，Raschka 著

圖書標籤:

• Python
• 機器學習
• 數據科學
• 人工智能
• 影印版
• 包郵
• Python編程
• 算法
• 數據分析
• 書籍

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齣版社： 東南大學齣版社

ISBN：9787564170776

商品編碼：22298661412

包裝：平裝

齣版時間：2017-04-01

基本信息

書名:Python機器學習(影印版)

定價：87.00元

作者:Sebastian,Raschka

齣版社：東南大學齣版社

齣版日期：2017-04-01

ISBN：9787564170776

字數：

頁碼：425

版次：1

裝幀：平裝

開本：16開

商品重量：0.4kg

內容提要

機器學習和預測分析正在改變商業和其他組織的運作模式。
　　《Python機器學習（影印版）》將帶你進入預測分析的世界，通過演示告訴你為什麼Python是世界數據科學語言之一。如果你想詢問更深入的數據問題，或是想增進、拓展機器學習係統的能力，這本實用的書籍可謂是無價之寶。
　　《Python機器學習（影印版）》涵蓋瞭包括scikit-learn、Theano和Keras在內的大量功能強大的Python庫、操作指南以及從情感分析到神經網絡的各色小技巧，很快你就能夠解答你個人及組織所麵對的那些*重要的問題。

目錄

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

作者介紹

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在機器學習領域的強大力量 機器學習，作為人工智能的核心驅動力，正以前所未有的速度滲透到我們生活的方方麵麵，從智能推薦係統到自動駕駛，再到醫療診斷和金融風控，其應用前景無垠。而Python，憑藉其簡潔的語法、豐富的庫生態和龐大的社區支持，已然成為機器學習領域最受青睞的編程語言。本書正是為那些渴望掌握Python機器學習精髓，或是希望在這一激動人心的領域開拓進取的讀者量身打造。 我們並非僅僅羅列枯燥的算法定義和晦澀的數學公式，而是力求以一種更具實踐導嚮、更富啓發性的方式，引領您一步步走進Python機器學習的精彩世界。本書將從最基礎的概念講起，逐步深入到復雜的模型和前沿的技術，確保您在掌握理論知識的同時，也能擁有解決實際問題的能力。 核心內容概覽： 第一部分：機器學習的基石——理解核心概念與準備工作 在正式踏上機器學習的旅程之前，充分的準備和對基本概念的深刻理解至關重要。本部分將為您打下堅實的基礎，讓您能夠更自信地迎接後續的學習挑戰。 Python基礎與環境搭建： 對於初次接觸Python的讀者，我們將快速迴顧Python的核心語法，包括數據類型、控製流、函數以及麵嚮對象編程的基本概念。更重要的是，我們將詳細指導您如何在您的操作係統上安裝和配置Python開發環境，包括常用的集成開發環境（IDE）如PyCharm、VS Code，以及包管理工具pip。我們將重點介紹如何安裝和管理至關重要的機器學習庫，如NumPy、Pandas和Matplotlib，它們將是您進行數據處理、科學計算和可視化分析的得力助手。 數據科學的利器：NumPy與Pandas： 在機器學習項目中，數據是原材料。NumPy為Python提供瞭強大的多維數組對象（ndarray），使其能夠高效地進行數值計算，這是許多機器學習算法的基礎。我們將深入講解NumPy的數組創建、索引、切片、數學運算以及廣播機製。Pandas庫則提供瞭更加靈活和強大的數據結構——Series和DataFrame，它們非常適閤處理錶格型數據，如CSV文件、數據庫記錄等。您將學習如何使用Pandas進行數據的讀取、清洗、轉換、閤並、分組以及缺失值處理，為後續的模型訓練做好準備。 數據可視化：Matplotlib與Seaborn： 一圖勝韆言。有效的數據可視化能夠幫助我們更好地理解數據分布、特徵之間的關係以及模型訓練的效果。Matplotlib是Python中最基礎但功能強大的繪圖庫，我們將學習如何繪製摺綫圖、散點圖、柱狀圖、直方圖等。在此基礎上，Seaborn庫提供瞭更高級、更美觀的統計圖形繪製功能，能夠輕鬆生成復雜的探索性數據分析圖錶，如熱力圖、箱綫圖、小提琴圖等，幫助您洞察數據中的隱藏模式。 第二部分：監督學習——從基礎到進階的經典模型 監督學習是機器學習中最常見也是應用最廣泛的一種類型，其目標是根據帶有標簽的訓練數據來預測未知數據的標簽。本部分將係統地介紹一係列經典的監督學習算法。 綫性迴歸與邏輯迴歸： 綫性迴歸是預測連續值變量的最簡單也是最基礎的模型，常用於分析變量之間的綫性關係。我們將講解綫性迴歸的原理、損失函數（如均方誤差）以及模型訓練的優化方法（如梯度下降）。邏輯迴歸雖然名字帶有“迴歸”，但它實際上是一個強大的分類算法，常用於二分類問題。我們將深入理解其Sigmoid函數、交叉熵損失函數以及如何將其推廣到多分類問題。 支持嚮量機（SVM）： 支持嚮量機是一種強大且靈活的分類模型，它通過找到最優的超平麵來分隔不同類彆的數據。我們將深入講解SVM的核心思想，包括最大間隔、核函數（綫性核、多項式核、高斯核RBF）以及軟間隔的概念，理解SVM如何處理非綫性可分的數據。 決策樹與隨機森林： 決策樹是一種直觀且易於理解的分類和迴歸模型，它通過一係列的“if-then”規則來做齣預測。我們將講解決策樹的構建過程，包括信息增益、基尼不純度等概念，以及如何避免過擬閤。隨機森林作為一種集成學習方法，通過構建多個決策樹並集成它們的預測結果，能夠顯著提高模型的魯棒性和準確性，是處理復雜數據集的強大武器。 K近鄰（KNN）： K近鄰算法是一種非參數的、基於實例的學習方法。它通過計算新樣本與訓練集中所有樣本的距離，並根據“少數服從多數”的原則進行分類（或取平均值進行迴歸）。我們將講解距離度量（歐氏距離、曼哈頓距離等）、K值的選擇以及KNN算法的優缺點。 樸素貝葉斯： 樸素貝葉斯算法基於貝葉斯定理，並假設特徵之間是相互獨立的（“樸素”的假設）。盡管存在這種簡化假設，樸素貝葉斯在文本分類等領域錶現齣色，且訓練速度快。我們將講解其概率模型和應用場景。 第三部分：無監督學習——挖掘數據中的隱藏結構 無監督學習則是在沒有預設標簽的情況下，讓模型自行發現數據中的模式、結構和關係。本部分將介紹幾種重要的無監督學習算法。 聚類算法：K-Means與層次聚類： 聚類是將相似的數據點分組到不同的簇中。K-Means是最經典和廣泛使用的聚類算法之一，我們將深入理解其迭代過程、簇中心的選擇以及如何評估聚類結果。層次聚類則能夠構建數據點的層級結構，生成聚類樹，提供不同粒度的聚類視圖。 降維技術：主成分分析（PCA）與t-SNE： 在高維數據集中，常常存在冗餘信息，導緻模型訓練效率低下或過擬閤。降維技術能夠將高維數據映射到低維空間，同時盡可能保留數據的重要信息。主成分分析（PCA）是一種綫性的降維方法，通過找到數據方差最大的方嚮來提取主要成分。t-SNE（t-Distributed Stochastic Neighbor Embedding）則是一種強大的非綫性降維技術，尤其擅長可視化高維數據，並能夠保持局部數據的結構。 關聯規則挖掘：Apriori算法： 關聯規則挖掘用於發現數據項之間的有趣關係，常用於市場籃子分析，如“購買瞭牛奶的顧客也常常購買麵包”。Apriori算法是經典的關聯規則挖掘算法，我們將學習如何生成頻繁項集和關聯規則。 第四部分：模型評估與調優——提升模型性能的關鍵 僅僅構建模型是不夠的，如何評估模型的性能並對其進行優化，是機器學習項目成功的關鍵。 模型評估指標： 對於分類問題，我們將學習準確率、精確率、召迴率、F1分數、ROC麯綫和AUC值等關鍵評估指標，理解它們各自的含義和適用場景。對於迴歸問題，我們將介紹均方誤差（MSE）、均方根誤差（RMSE）、平均絕對誤差（MAE）以及R²分數等。 交叉驗證： 為瞭更可靠地評估模型的泛化能力，避免過擬閤，交叉驗證是一種必不可少的技術。我們將講解K摺交叉驗證、留一法交叉驗證等常用方法。 超參數調優：網格搜索與隨機搜索： 大多數機器學習模型都存在超參數，需要手動調整纔能達到最優性能。網格搜索會窮舉所有可能的參數組閤，而隨機搜索則以隨機的方式搜索參數空間，兩者都是有效的調優策略。 正則化技術：L1與L2正則化： 正則化是一種防止模型過擬閤的有效手段，它通過在損失函數中添加懲罰項來約束模型的復雜度。我們將深入理解L1（Lasso）和L2（Ridge）正則化的原理及其對模型參數的影響。 第五部分：深度學習入門——探索神經網絡的奧秘 隨著人工智能的飛速發展，深度學習成為瞭機器學習領域的熱門話題。本部分將為您開啓深度學習的大門。 神經網絡基礎： 我們將從最基本的感知機開始，逐步介紹多層感知機（MLP）、激活函數（ReLU、Sigmoid、Tanh）、前嚮傳播與反嚮傳播算法，以及損失函數和優化器。 TensorFlow與Keras： TensorFlow是Google開發的一款強大的深度學習框架，而Keras則是一個用戶友好的高級API，它能夠運行在TensorFlow之上，極大地簡化瞭深度學習模型的構建和訓練過程。我們將指導您使用Keras構建和訓練各種類型的神經網絡，包括捲積神經網絡（CNN）和循環神經網絡（RNN）。 本書的特色： 理論與實踐並重： 我們不僅深入淺齣地講解機器學習的理論概念，更注重通過豐富的Python代碼示例來演示如何將這些理論應用於實際問題。 循序漸進的學習路徑： 從基礎概念到高級模型，本書的設計遵循邏輯清晰、難度遞增的學習路徑，讓您能夠逐步建立對機器學習的全麵認識。 豐富的數據集與項目實戰： 書中將提供多種真實世界的數據集，並引導您完成一係列具有代錶性的機器學習項目，讓您在實戰中鞏固所學知識。 前沿技術探討： 在對經典模型進行深入講解的同時，本書也將適時引入深度學習等前沿技術，幫助您瞭解機器學習的最新發展趨勢。 無論您是希望入門機器學習的初學者，還是希望提升技能的開發者，抑或是對人工智能充滿好奇的探索者，本書都將是您不可或缺的學習伴侶。讓我們一起，用Python驅動智能，探索機器學習的無限可能！

評分☆☆☆☆☆

作為一名有一定Python基礎但對機器學習感到迷茫的開發者，我一直在尋找一本能夠係統性地梳理機器學習知識體係並提供實用指導的書。這本書的結構安排真的非常閤理。它從機器學習的基本概念、數據預處理、特徵工程開始，然後逐步深入到各種主流的算法模型，包括監督學習、無監督學習、半監督學習，甚至還涉及瞭深度學習的入門。最讓我滿意的是，書中並沒有迴避復雜的數學概念，而是以一種循序漸進的方式介紹，並輔以大量的圖示和代碼示例，這讓原本抽象的理論變得生動易懂。每介紹完一個算法，都會有相應的代碼實現，而且這些代碼都經過優化，可以直接用於實際項目。我還特彆喜歡書中關於模型評估和調優的部分，這對於避免“過擬閤”和“欠擬閤”等常見問題至關重要，也讓我學到瞭很多實用的技巧。讀完這本書，我感覺自己不僅掌握瞭理論知識，還具備瞭獨立解決實際機器學習問題的能力。

評分☆☆☆☆☆

這本書簡直是新手入門的福音！我之前對機器學習完全是零基礎，看著各種高大上的概念就頭疼。但是，《Python機器學習（影印版）》的講解方式真的非常親民。作者從最基礎的Python環境搭建講起，一步步引導讀者熟悉常用的庫，比如NumPy、Pandas和Matplotlib，這些都是後續學習機器學習的基石，講得非常紮實。而且，書中對於每一個算法的講解，都配有清晰的數學公式推導，但同時又不會讓人生畏，它會用非常直觀的比喻和圖示來解釋這些公式背後的邏輯，讓人一下子就能理解。我尤其喜歡它在講解監督學習和無監督學習時，舉的那些貼近生活的例子，比如垃圾郵件分類、客戶細分等等，讓我覺得機器學習離我並不遙遠，而是可以解決實際問題的工具。書中的代碼示例也非常完整，我可以直接復製運行，並且有詳細的注釋，即使我不完全理解每一個細節，也能跟著書的節奏走。讀完前麵的章節，我感覺自己對機器學習的整體框架有瞭一個初步的認識，不再是那個對“黑箱”感到恐懼的小白瞭。

評分☆☆☆☆☆

我是一名對數據分析有濃厚興趣的職場人士，一直想將機器學習技術應用到我的工作中，但苦於沒有閤適的入門教材。《Python機器學習（影印版）》真的給瞭我很大的驚喜。它以非常友好和易於理解的方式，講解瞭機器學習的核心概念和常用算法。書中的例子非常貼閤實際應用場景，比如如何進行數據清洗、特徵提取，以及如何構建一個預測模型。我尤其喜歡它對scikit-learn庫的講解，這是我之前接觸較少但非常重要的一個庫，通過這本書，我能夠快速掌握它的基本用法，並將其應用到我的數據分析項目中。書中對於模型評估和參數調優的介紹也讓我受益匪淺，讓我能夠更有效地衡量模型的性能，並找到最佳的模型配置。總的來說，這本書為我打開瞭機器學習應用的大門，讓我能夠更自信地運用這項技術來解決實際業務問題。

評分☆☆☆☆☆

我是一名在校的計算機專業學生，一直對數據科學和人工智能領域很感興趣，尤其是機器學習。在選擇學習資料時，我傾嚮於一些能夠深入理解理論基礎，並且能夠快速上手實踐的書籍。《Python機器學習（影印版）》就完美契閤瞭我的需求。它在理論深度上做得相當不錯，對於像綫性迴歸、邏輯迴歸、決策樹、支持嚮量機（SVM）、神經網絡等經典算法，都有非常詳盡的講解，不僅介紹瞭算法的思想，還深入剖析瞭其背後的數學原理和優缺點。更重要的是，這本書沒有停留在理論層麵，而是提供瞭大量的Python代碼實現，並且使用瞭scikit-learn等主流庫，這對於我們這些需要將理論應用於實際項目學習的學生來說，是極其寶貴的。書中的案例分析也很有針對性，覆蓋瞭分類、迴歸、聚類等多種機器學習任務，讓我能夠通過實際操作來鞏固理論知識。讀完這本書，我感覺自己在理解機器學習算法的本質以及如何運用Python進行實現方麵，都有瞭質的飛躍。

評分☆☆☆☆☆

這本書讓我對機器學習的理解不再是碎片化的。之前我零散地接觸過一些機器學習的算法，但總覺得像是東拼西湊，缺乏一個完整的體係。《Python機器學習（影印版）》恰好填補瞭我的這個空白。它以清晰的脈絡，從機器學習的“是什麼”、“為什麼”到“怎麼做”，層層遞進，將復雜的知識點梳理得井井有條。我尤其欣賞它在介紹各種模型時，不僅僅是給齣代碼，而是花瞭很大篇幅解釋算法的原理、假設以及適用場景，這有助於我理解在不同問題下應該選擇哪種模型。書中提供的代碼示例非常高質量，不僅僅是功能實現，還體現瞭良好的編程風格和實踐經驗，這對我學習如何寫齣可維護、可擴展的代碼非常有幫助。此外，對於模型的評估和選擇，書中也給齣瞭非常實用的建議和方法，這讓我擺脫瞭“盲目嘗試”的睏境，能夠更科學地進行模型開發。