Python机器学习（影印版）
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作者(美)塞巴斯蒂安·拉什卡
出版社东南大学出版社
ISBN9787564170776
出版时间2017-05
装帧平装
开本16开
纸张胶版纸
定价87元
货号25084232
上书时间2024-06-30
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【内容简介】:
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。
由塞巴斯蒂安·拉什卡*的《Python机器学习( 影印版)(英文版)》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**的数据科学语言之一。如果你想询问*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。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*重要的问题。
【目录】:
PrefaceChapter 1: Givin Com uters 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 SummaryChapter 2: Training Machine Learning Algorithmsfor 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 SummaryChapter 3: A Tour of Machine Learning Classifiers UsingScikit-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 SummaryChapter 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 SummaryChapter 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 SummaryChapter 6: Learning Best Practices for Model Evaluationand 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 SummaryChapter 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 SummaryChapter 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 SummaryChapter 9: Embedding a Machine Learning Model intoa 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 SummaryChapter 10: Predicting Continuous Target Variableswith 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 SummaryChapter 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 SummaryChapter 12: Training Artificial Neural Networks for ImageRecognition 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 SummaryChapter 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 SummaryIndex
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