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【包邮】 Python机器学习（影印版）【正版九新】

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作者(美)塞巴斯蒂安·拉什卡

出版社东南大学

ISBN9787564170776

出版时间2017-04

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开本16开

定价87元

货号9787564170776

上书时间2024-11-14

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商品描述: 作者简介
Sebastian Raschka is 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.

目录
Preface
Chapter 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
  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
  Si