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

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