最新章節

• Summary
• Best practice 18 - update models regularly
• Best practice 17 - monitor model performance
• Best practice 16 - save load and reuse models
• Best practices in the deployment and monitoring stage
• Best practice 15 - diagnose overfitting and underfitting

• Summary 更新時間：2021-07-02 22:57:40
• Best practice 18 - update models regularly
• Best practice 17 - monitor model performance
• Best practice 16 - save load and reuse models
• Best practices in the deployment and monitoring stage
• Best practice 15 - diagnose overfitting and underfitting
• Best practice 14 - reduce overfitting
• Neural networks
• Random forest (or decision tree)
• SVM
• Logistic regression
• Naive Bayes
• Best practice 13 - choose the right algorithm(s) to start with
• Best practices in the model training evaluation and selection stage
• Best practice 12 - document how each feature is generated
• Best practice 11 - perform feature engineering without domain expertise
• Best practice 10 - perform feature engineering with domain expertise
• Best practice 9 - decide on whether or not to scale features
• Best practice 8 - decide on whether or not to reduce dimensionality and if so how
• Best practice 7 - decide on whether or not to select features and if so how
• Best practice 6 - decide on whether or not to encode categorical features
• Best practice 5 - determine categorical features with numerical values
• Best practices in the training sets generation stage
• Best practice 4 - deal with missing data
• Best practice 3 - maintain consistency of field values
• Best practice 2 - collect all fields that are relevant
• Best practice 1 - completely understand the project goal
• Best practices in the data preparation stage
• Machine learning workflow
• Best Practices
• Summary
• Stock price prediction with regression algorithms
• Regression performance evaluation
• Support vector regression
• Decision tree regression
• Linear regression
• Data acquisition and feature generation
• Feature engineering
• Predicting stock price with regression algorithms
• What is regression?
• Brief overview of the stock market and stock price
• Stock Price Prediction with Regression Algorithms
• Summary
• Feature selection via random forest
• Handling multiclass classification
• Training on large-scale datasets with online learning
• Training a logistic regression model with regularization
• Training a logistic regression model via stochastic gradient descent
• Click-through prediction with logistic regression by gradient descent
• Training a logistic regression model via gradient descent
• The mechanics of logistic regression
• Getting started with the logistic function
• Logistic regression classifier
• One-hot encoding - converting categorical features to numerical
• Click-Through Prediction with Logistic Regression
• Summary
• Random forest - feature bagging of decision tree
• Click-through prediction with decision tree
• The implementations of decision tree
• The metrics to measure a split
• The construction of a decision tree
• Decision tree classifier
• Getting started with two types of data numerical and categorical
• Brief overview of advertising click-through prediction
• Click-Through Prediction with Tree-Based Algorithms
• Summary
• More examples - fetal state classification on cardiotocography with SVM
• News topic classification with support vector machine
• Choosing between the linear and RBF kernel
• The kernels of SVM
• Scenario 4 - dealing with more than two classes
• The implementations of SVM
• Scenario 3 - handling outliers
• Scenario 2 - determining the optimal hyperplane
• Scenario 1 - identifying the separating hyperplane
• The mechanics of SVM
• Support vector machine
• Recap and inverse document frequency
• News Topic Classification with Support Vector Machine
• Summary
• Model tuning and cross-validation
• Classifier performance evaluation
• The naive Bayes implementations
• The mechanics of naive Bayes
• Bayes' theorem by examples
• Exploring naive Bayes
• Applications of text classification
• Types of classification
• Getting started with classification
• Spam Email Detection with Naive Bayes
• Summary
• Topic modeling
• Clustering
• Data preprocessing
• Visualization
• Thinking about features
• Getting the data
• The newsgroups data
• Touring powerful NLP libraries in Python
• What is NLP?
• Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
• Summary
• Troubleshooting and asking for help
• Installing software and setting up
• Voting and averaging
• Blending
• Stacking
• Boosting
• Bagging
• Combining models
• Binning
• Power transformations
• Polynomial features
• Scaling
• One-hot-encoding
• Label encoding
• Missing values
• Preprocessing exploration and feature engineering
• Avoid overfitting with feature selection and dimensionality reduction
• Avoid overfitting with regularization
• Avoid overfitting with cross-validation
• Overfitting underfitting and the bias-variance tradeoff
• Generalizing with data
• A brief history of the development of machine learning algorithms
• A very high level overview of machine learning
• What is machine learning and why do we need it?
• Getting Started with Python and Machine Learning
• Questions
• Piracy
• Errata
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• www.PacktPub.com
• About the Reviewer
• About the Author
• Credits
• Title Page
• coverpage

• 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 更新時間：2021-07-02 22:57:40