更新时间：2021-07-02 22:57:40
coverpage
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
Getting Started with Python and Machine Learning
What is machine learning and why do we need it?
A very high level overview of machine learning
A brief history of the development of machine learning algorithms
Generalizing with data
Overfitting underfitting and the bias-variance tradeoff
Avoid overfitting with cross-validation
Avoid overfitting with regularization
Avoid overfitting with feature selection and dimensionality reduction
Preprocessing exploration and feature engineering
Missing values
Label encoding
One-hot-encoding
Scaling
Polynomial features
Power transformations
Binning
Combining models
Bagging
Boosting
Stacking
Blending
Voting and averaging
Installing software and setting up
Troubleshooting and asking for help
Summary
Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
What is NLP?
Touring powerful NLP libraries in Python
The newsgroups data
Getting the data
Thinking about features
Visualization
Data preprocessing
Clustering
Topic modeling
Spam Email Detection with Naive Bayes
Getting started with classification
Types of classification
Applications of text classification
Exploring naive Bayes
Bayes' theorem by examples
The mechanics of naive Bayes
The naive Bayes implementations
Classifier performance evaluation
Model tuning and cross-validation
News Topic Classification with Support Vector Machine
Recap and inverse document frequency
Support vector machine
The mechanics of SVM
Scenario 1 - identifying the separating hyperplane
Scenario 2 - determining the optimal hyperplane
Scenario 3 - handling outliers
The implementations of SVM
Scenario 4 - dealing with more than two classes
The kernels of SVM
Choosing between the linear and RBF kernel
News topic classification with support vector machine
More examples - fetal state classification on cardiotocography with SVM
Click-Through Prediction with Tree-Based Algorithms
Brief overview of advertising click-through prediction
Getting started with two types of data numerical and categorical
Decision tree classifier
The construction of a decision tree
The metrics to measure a split
The implementations of decision tree
Click-through prediction with decision tree
Random forest - feature bagging of decision tree
Click-Through Prediction with Logistic Regression
One-hot encoding - converting categorical features to numerical
Logistic regression classifier
Getting started with the logistic function
The mechanics of logistic regression
Training a logistic regression model via gradient descent
Click-through prediction with logistic regression by gradient descent
Training a logistic regression model via stochastic gradient descent
Training a logistic regression model with regularization
Training on large-scale datasets with online learning
