Python 语言构建机器学习系统 第2版（影印版）

Chapter 1: Getting Started with Python Machine Learning

Machine learning and Python - a dream team

What the book will teach you (and what it will not)

What to do when you are stuck

Getting started

Introduction to NumPy, SciPy, and matplotlib

Installing Python

Chewing data efficiently with NumPy and intelligentlywith SciPy

Learning NumPy

Indexing

Handling nonexisting values

Comparing the runtime

Learning SciPy

Our first (tiny) application of machine learning

Reading in the data

Preprocessing and cleaning the data

Choosing the right model and learning algorithm

Beforebuilding our first model...

Starting with a simple straight line

Towards some advanced stuff

Stepping back to go forward - another look at our data

Training and testing

Answering our initial question

Summary

Chapter 2: Classifying with Real-world Examples

The Iris dataset

Visualization is a good first step

Building our first classification model

Evaluation - holding out data and cross-validation

Building more complex classifiers

A more complex dataset and a more complex classifim

Learning about the Seeds dataset

Features and feature engineering

Nearest neighbor classification

Classifying with scikit-learn

Looking at the decision boundaries

Binary and multiclass classification

Summary

Chapter 3: Clustering - Finding Related Posts

Measuring the relatedness of posts

How not to do it

How to do it

Preprocessing - similarity measured as a similar number of common words

Converting raw text into a bag of words

Counting words

Normalizing word count vectors

Removing less important words

Stemming

Stop words on steroids

Our achievements and goals

Clustering

K-means

Getting test data to evaluate our ideas on

Clustering posts

Solving our initial challenge

Another look at noise

Tweaking the parameters

Summary

Chapter 4: Topic Modeling

Latent Dirichlet allocation

Building a topic model

Comparing documents by topics

Modeling the whole of Wikipedia

Choosing the number of topics

Summary

Chapter 5: Classification - Detecting Poor Answers

Sketching our roadmap

Learning to classify classy answers

Tuning the instance

Tuning the classifier

Fetching the data

Slimming the data down to chewable chunks

Preselection and processing of attributes

Defining what is a good answer

Creating our first classifier

Starting with kNN

Engineering the features

Training the classifier

Measuring the classifier's performance

Designing more features

Deciding how to improve

Bias-variance and their tradeoff

Fixing high bias

Fixing high variance

High bias or low bias

Using logistic regression

A bit of math with a small example

Applying logistic regression to our post classification problem

Looking behind accuracy- precision and recall

Slimming the classifier

Ship it!

Summary

Chapter 6: Classification II - Sentiment Analysis

Sketching our roadmap

Fetching the Twitter data

Introducing the Naive Bayes classifier

Getting to know the Bayes' theorem

Being naive

Using Naive Bayes to classify

Accounting for unseen words and other oddities

Accounting for arithmetic underflows

Creating our first classifier and tuning it

Solving an easy problem first

Using all classes

Tuning the classifier's parameters

Cleaning tweets

Taking the word types into account

Determining the word types

Successfully cheating using SentiWordNet

Our first estimator

Putting everything together

Summary

Chapter 7: Regression

Predicting house prices with regression

Multidimensional regression

Cross-validation for regression

Penalized or regularized regression

L1 and L2 penalties

Using Lasso or ElasticNet in scikit-learn

Visualizing the Lasso path

P-greater-than-N scenariOS

An example based on text documents

Setting hyperparameters in a principled way

Summary

Chapter 8: Recommendations

Rating predictions and recommendations

Splitting into training and testing

Normalizing the training data

A neighborhood approach to recommendations

A regression approach to recommendations

Combining multiple methods

Basket analysis

Obtaining useful predictions

Analyzing supermarket shopping baskets

Association rule mining

More advanced basket analysis

Summary

Chapter 9: Classification - Music Genre Classification

Sketching our roadmap

Fetching the music data

Converting into a WAV format

Looking at music

Decomposing music into sine wave components

Using FFT to build our first classifier

Increasing experimentation agility

Training the classifier

Using a confusion matrix to measure accuracy in

multiclass problems

An alternative way to measure classifier performance

using receiver-operator characteristics

Improving classification performance with Mel

Frequency Cepstral Coefficients

Summary

Chapter 10: Computer Vision

Introducing image processing

Loading and displaying images

Thresholding

Gaussian blurring

Putting the center in focus

Basic image classification

Computing features from images

Writing your own features

Using features to find similar images

Classifying a harder dataset

Local feature representations

Summary

Chapter 11: Dimensionality Reduction

Sketching our roadmap

Selecting features

Detecting redundant features using filters

Correlation

Mutual information

Asking the model about the features using wrappers

Other feature selection methods

Feature extraction

About principal component analysis

Sketching PCA

Applying PCA

Limitations of PCAand how LDA can help

Multidimensional scaling

Summary

Chapter 12: Bigger Data

Learning about big data

Using jug to break up your pipeline into tasks

An introduction to tasks in jug

Looking under the hood

Using jug for data analysis

Reusing partial results

Using Amazon web Services

Creating your first virtual machines

Installing Python packages on Amazon Linux

Running jug on our cloud machine

Automating the generation of clusters with StarCluster

Summary

Appendix: Where to Learn More Machine Learning

Online courses

Books

Question and answer sites

Blogs

Data sources

Getting competitive

All that was left out

Summary

Index