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Python Machine Learning(2nd) 无水印原版pdf.pdf
Cover
Copyright
About the Authors
Table of Contents
Preface
1: Giving Computers 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
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 and packages from the Python Package Index
Using the Anaconda Python distribution and package manager
Packages for scientific computing, data science, and machine learning
Summary
2: Training Simple Machine Learning Algorithms for Classification
Artificial neurons – a brief glimpse into the early history of machine learning
The formal definition of an artificial neuron
The perceptron learning rule
Implementing a perceptron learning algorithm in Python
An object-oriented perceptron API
Training a perceptron model on the Iris dataset
Adaptive linear neurons and the convergence of learning
Minimizing cost functions with gradient descent
Implementing Adaline in Python
Improving gradient descent through feature scaling
Large-scale machine learning and stochastic gradient descent
Summary
3: A Tour of Machine Learning Classifiers Using scikit-learn
Choosing a classification algorithm
First steps with scikit-learn – training a perceptron
Modeling class probabilities via logistic regression
Logistic regression intuition and conditional probabilities
Learning the weights of the logistic cost function
Converting an Adaline implementation into an algorithm for logistic regression
Training a logistic regression model with scikit-learn
Tackling overfitting via regularization
Maximum margin classification with support vector machines
Maximum margin intuition
Dealing with a nonlinearly separable case using slack variables
Alternative implementations in scikit-learn
Solving nonlinear problems using a kernel SVM
Kernel methods for linearly inseparable data
Using the kernel trick to find separating hyperplanes in high-dimensional space
Decision tree learning
Maximizing information gain – getting the most bang for your buck
Building a decision tree
Combining multiple decision trees via random forests
K-nearest neighbors – a lazy learning algorithm
Summary
4: Building Good Training Sets – Data Preprocessing
Dealing with missing data
Identifying missing values in tabular data
Eliminating samples or features with missing values
Imputing missing values
Understanding the scikit-learn estimator API
Handling categorical data
Nominal and ordinal features
Creating an example dataset
Mapping ordinal features
Encoding class labels
Performing one-hot encoding on nominal features
Partitioning a dataset into separate training and test sets
Bringing features onto the same scale
Selecting meaningful features
L1 and L2 regularization as penalties against model complexity
A geometric interpretation of L2 regularization
Sparse solutions with L1 regularization
Sequential feature selection algorithms
Assessing feature importance with random forests
Summary
5: Compressing Data via Dimensionality Reduction
Unsupervised dimensionality reduction via principal component analysis
The main steps behind principal component analysis
Extracting the principal components step by step
Total and explained variance
Feature transformation
Principal component analysis in scikit-learn
Supervised data compression via linear discriminant analysis
Principal component analysis versus linear discriminant analysis
The inner workings of 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
6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
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 over- 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
Scoring metrics for multiclass classification
Dealing with class imbalance
Summary
7: Combining Different Models for Ensemble Learning
Learning with ensembles
Combining classifiers via majority vote
Implementing a simple majority vote classifier
Using the majority voting principle to make predictions
Evaluating and tuning the ensemble classifier
Bagging – building an ensemble of classifiers from bootstrap samples
Bagging in a nutshell
Applying bagging to classify samples in the Wine dataset
Leveraging weak learners via adaptive boosting
How boosting works
Applying AdaBoost using scikit-learn
Summary
8: Applying Machine Learning to Sentiment Analysis
Preparing the IMDb movie review data for text processing
Obtaining the movie review dataset
Preprocessing the movie dataset into more convenient format
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
Topic modeling with Latent Dirichlet Allocation
Decomposing text documents with LDA
LDA with scikit-learn
Summary
9: Embedding a Machine Learning Model into a Web Application
Serializing fitted scikit-learn estimators
Setting up an SQLite database for data storage
Developing a web application with Flask
Our first Flask web application
Form validation and rendering
Setting up the directory structure
Implementing a macro using the Jinja2 templating engine
Adding style via CSS
Creating the result page
Turning the movie review classifier into a web application
Files and folders – looking at the directory tree
Implementing the main application as app.py
Setting up the review form
Creating a results page template
Deploying the web application to a public server
Creating a PythonAnywhere account
Uploading the movie classifier application
Updating the movie classifier
Summary
10: Predicting Continuous Target Variables with Regression Analysis
Introducing linear regression
Simple linear regression
Multiple linear regression
Exploring the Housing dataset
Loading the Housing dataset into a data frame
Visualizing the important characteristics of a dataset
Looking at relationships using a correlation matrix
Implementing an ordinary least squares linear regression model
Solving regression for regression parameters with gradient descent
Estimating 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
Adding polynomial terms using scikit-learn
Modeling nonlinear relationships in the Housing dataset
Dealing with nonlinear relationships using random forests
Decision tree regression
Random forest regression
Summary
11: Working with Unlabeled Data – Clustering Analysis
Grouping objects by similarity using k-means
K-means clustering using scikit-learn
A smarter way of placing the initial cluster centroids using 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
Grouping clusters in bottom-up fashion
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
12: Implementing a Multilayer Artificial Neural Network from Scratch
Modeling complex functions with artificial neural networks
Single-layer neural network recap
Introducing the multilayer neural network architecture
Activating a neural network via forward propagation
Classifying handwritten digits
Obtaining the MNIST dataset
Implementing a multilayer perceptron
Training an artificial neural network
Computing the logistic cost function
Developing your intuition for backpropagation
Training neural networks via backpropagation
About the convergence in neural networks
A few last words about the neural network implementation
Summary
13: Parallelizing Neural Network Training with TensorFlow
TensorFlow and training performance
What is TensorFlow?
How we will learn TensorFlow
First steps with TensorFlow
Working with array structures
Developing a simple model with the low-level TensorFlow API
Training neural networks efficiently with high-level TensorFlow APIs
Building multilayer neural networks using TensorFlow's Layers API
Developing a multilayer neural network with Keras
Choosing activation functions for multilayer networks
Logistic function recap
Estimating class probabilities in multiclass classification via the softmax function
Broadening the output spectrum using a hyperbolic tangent
Rectified linear unit activation
Summary
14: Going Deeper – The Mechanics of TensorFlow
Key features of TensorFlow
TensorFlow ranks and tensors
How to get the rank and shape of a tensor
Understanding TensorFlow's computation graphs
Placeholders in TensorFlow
Defining placeholders
Feeding placeholders with data
Defining placeholders for data arrays with varying batchsizes
Variables in TensorFlow
Defining variables
Initializing variables
Variable scope
Reusing variables
Building a regression model
Executing objects in a TensorFlow graph using their names
Saving and restoring a model in TensorFlow
Transforming Tensors as multidimensional data arrays
Utilizing control flow mechanics in building graphs
Visualizing the graph with TensorBoard
Extending your TensorBoard experience
Summary
15: Classifying Images with Deep Convolutional Neural Networks
Building blocks of convolutional neural networks
Understanding CNNs and learning feature hierarchies
Performing discrete convolutions
Performing a discrete convolution in one dimension
The effect of zero-padding in a convolution
Determining the size of the convolution output
Performing a discrete convolution in 2D
Subsampling
Putting everything together to build a CNN
Working with multiple input or color channels
Regularizing a neural network with dropout
Implementing a deep convolutional neural network using TensorFlow
The multilayer CNN architecture
Loading and preprocessing the data
Implementing a CNN in the TensorFlow low-level API
Implementing a CNN in the TensorFlow layers API
Summary
16: Modeling Sequential Data Using Recurrent Neural Networks
Introducing sequential data
Modeling sequential data – order matters
Representing sequences
The different categories of sequence modeling
RNNs for modeling sequences
Understanding the structure and flow of an RNN
Computing activations in an RNN
The challenges of learning long-range interactions
LSTM units
Implementing a multilayer RNN for sequence modeling in TensorFlow
Project one – performing sentiment analysis of IMDb movie reviews using multilayer RNNs
Preparing the data
Embedding
Building an RNN model
The SentimentRNN class constructor
The build method
Step 1 – defining multilayer RNN cells
Step 2 – defining the initial states for the RNN cells
Step 3 – creating the RNN using the RNN cells and their states
The train method
The predict method
Instantiating the SentimentRNN class
Training and optimizing the sentiment analysis RNN model
Project two – implementing an RNN for character-level language modeling in TensorFlow
Preparing the data
Building a character-level RNN model
The constructor
The build method
The train method
The sample method
Creating and training the CharRNN Model
The CharRNN model in the sampling mode
and book summary
Index
[ 1 ]
Python Machine Learning
Second Edition
Machine Learning and Deep Learning with Python,
scikit-learn, and TensorFlow
Sebastian Raschka
Vahid Mirjalili
BIRMINGHAM - MUMBAI
Python Machine Learning
Second Edition
Copyright © 2017 Packt Publishing
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First published: September 2015
Second edition: September 2017
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Published by Packt Publishing Ltd.
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Credits
Authors
Sebastian Raschka
Vahid Mirjalili
Reviewers
Jared Huffman
Huai-En, Sun (Ryan Sun)
Acquisition Editor
Frank Pohlmann
Project Coordinator
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Proofreader
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Indexer
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Technical Editors
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About the Authors
Sebastian Raschka, the author of the bestselling book, Python Machine Learning,
has many years of experience with coding in Python, and he has given several
seminars on the practical applications of data science, machine learning, and deep
learning including a machine learning tutorial at SciPy—the leading conference for
scientific computing in Python.
While Sebastian's academic research projects are mainly centered around
problem-solving in computational biology, he loves to write and talk about
data science, machine learning, and Python in general, and he is motivated to
help people develop data-driven solutions without necessarily requiring a machine
learning background.
His work and contributions have recently been recognized by the departmental
outstanding graduate student award 2016-2017 as well as the ACM Computing
Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open
source projects, and the methods that he has implemented are now successfully used
in machine learning competitions, such as Kaggle.
I would like to take this opportunity to thank the great Python
community and developers of open source packages who helped
me create the perfect environment for scientific research and data
science. Also, I want to thank my parents who always encouraged
and supported me in pursuing the path and career that I was so
passionate about.
Special thanks to the core developers of scikit-learn. As a contributor
to this project, I had the pleasure to work with great people who are
not only very knowledgeable when it comes to machine learning but
are also excellent programmers. Lastly, I'd like to thank Elie Kawerk,
who volunteered to review the book and provided valuable feedback
on the new chapters.
Vahid Mirjalili obtained his PhD in mechanical engineering working on novel
methods for large-scale, computational simulations of molecular structures.
Currently, he is focusing his research efforts on applications of machine learning
in various computer vision projects at the department of computer science and
engineering at Michigan State University.
Vahid picked Python as his number-one choice of programming language, and
throughout his academic and research career he has gained tremendous experience
with coding in Python. He taught Python programming to the engineering class at
Michigan State University, which gave him a chance to help students understand
different data structures and develop efficient code in Python.
While Vahid's broad research interests focus on deep learning and computer vision
applications, he is especially interested in leveraging deep learning techniques to
extend privacy in biometric data such as face images so that information is not
revealed beyond what users intend to reveal. Furthermore, he also collaborates with
a team of engineers working on self-driving cars, where he designs neural network
models for the fusion of multispectral images for pedestrian detection.
I would like to thank my PhD advisor, Dr. Arun Ross, for giving me
the opportunity to work on novel problems in his research lab. I also
like to thank Dr. Vishnu Boddeti for inspiring my interests in deep
learning and demystifying its core concepts.
About the Reviewers
Jared Huffman is an entrepreneur, gamer, storyteller, machine learning fanatic,
and database aficionado. He has dedicated the past 10 years to developing software
and analyzing data. His previous work has spanned a variety of topics, including
network security, financial systems, and business intelligence, as well as web
services, developer tools, and business strategy. Most recently, he was the founder
of the data science team at Minecraft, with a focus on big data and machine learning.
When not working, you can typically find him gaming or enjoying the beautiful
Pacific Northwest with friends and family.
I'd like to thank Packt for giving me the opportunity to work on
such a great book, my wife for the constant encouragement, and my
daughter for sleeping through most of the late nights while I was
reviewing and debugging code.
Huai-En, Sun (Ryan Sun) holds a master's degree in statistics from the National
Chiao Tung University. He is currently working as a data scientist for analyzing the
production line at PEGATRON. Machine learning and deep learning are his main
areas of research.
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