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Python Machine Learning By Example

Yuxi (Hayden) Liu 著

更新時(shí)間：2021-07-02 22:57:40

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最新章節(jié)： Summary

計(jì)算機(jī)網(wǎng)絡(luò) 編程語言與程序設(shè)計(jì)

Thisbookisforanyoneinterestedinenteringthedatasciencestreamwithmachinelearning.BasicfamiliaritywithPythonisassumed.

目錄(145章)

倒序

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
Summary
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
Summary
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
Summary
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
Summary
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
Handling multiclass classification
Feature selection via random forest
Summary
Stock Price Prediction with Regression Algorithms
Brief overview of the stock market and stock price
What is regression?
Predicting stock price with regression algorithms
Feature engineering
Data acquisition and feature generation
Linear regression
Decision tree regression
Support vector regression
Regression performance evaluation
Stock price prediction with regression algorithms
Summary
Best Practices
Machine learning workflow
Best practices in the data preparation stage
Best practice 1 - completely understand the project goal
Best practice 2 - collect all fields that are relevant
Best practice 3 - maintain consistency of field values
Best practice 4 - deal with missing data
Best practices in the training sets generation stage
Best practice 5 - determine categorical features with numerical values
Best practice 6 - decide on whether or not to encode categorical features
Best practice 7 - decide on whether or not to select features and if so how
Best practice 8 - decide on whether or not to reduce dimensionality and if so how
Best practice 9 - decide on whether or not to scale features
Best practice 10 - perform feature engineering with domain expertise
Best practice 11 - perform feature engineering without domain expertise
Best practice 12 - document how each feature is generated
Best practices in the model training evaluation and selection stage
Best practice 13 - choose the right algorithm(s) to start with
Naive Bayes
Logistic regression
SVM
Random forest (or decision tree)
Neural networks
Best practice 14 - reduce overfitting
Best practice 15 - diagnose overfitting and underfitting
Best practices in the deployment and monitoring stage
Best practice 16 - save load and reuse models
Best practice 17 - monitor model performance
Best practice 18 - update models regularly
Summary 更新時(shí)間：2021-07-02 22:57:40

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Python Machine Learning By Example