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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Summary
• Latent semantic analysis
• SVD versus PCA components
• The clustering model

• Leave a review - let other readers know what you think 更新時間：2021-06-25 22:46:20
• Other Books You May Enjoy
• Summary
• Latent semantic analysis
• SVD versus PCA components
• The clustering model
• Exploration of the data
• Hotel review data
• Applications of text clustering
• Case study 2 - predicting topics of hotel reviews data
• Applied facial recognition
• Some data exploration
• The data
• Applications of facial recognition
• Case study 1 - facial recognition
• Case Studies
• Summary
• Application of word embeddings - information retrieval
• The gensim package for creating Word2vec embeddings
• Word2Vec - another shallow neural network
• Two approaches to word embeddings - Word2vec and GloVe
• Word embeddings
• Learning text features – word vectorizations
• Using a linear model on extracted RBM components
• Using a linear model on extracted PCA components
• Using a linear model on raw pixel values
• Using RBMs in a machine learning pipeline
• Extracting RBM components from MNIST
• Extracting PCA components from MNIST
• The BernoulliRBM
• MNIST dataset
• Reconstructing the data
• The restriction of a Boltzmann Machine
• The graph of a Restricted Boltzmann Machine
• Not necessarily dimension reduction
• Restricted Boltzmann Machines
• The algorithms of this chapter
• Non-parametric fallacy
• Parametric assumptions of data
• Feature Learning
• Summary
• LDA versus PCA – iris dataset
• How to use LDA in scikit-learn
• Using the top eigenvectors to project onto the new space
• Keeping the top k eigenvectors by ordering them by descending eigenvalues
• Calculating eigenvalues and eigenvectors for SW-1SB
• Calculating within-class and between-class scatter matrices
• Calculating the mean vectors of each class
• How LDA works
• Linear Discriminant Analysis
• A deeper look into the principal components
• How centering and scaling data affects PCA
• Scikit-learn's PCA
• Using the kept eigenvectors to transform new data-points
• Keeping the top k eigenvalues (sorted by the descending eigenvalues)
• Calculating the eigenvalues of the covariance matrix
• Creating the covariance matrix of the dataset
• PCA with the Iris dataset – manual example
• How PCA works
• Principal Component Analysis
• Dimension reduction – feature transformations versus feature selection versus feature construction
• Feature Transformations
• Summary
• Choosing the right feature selection method
• Linear model coefficients as another feature importance metric
• A brief introduction to regularization
• Linear models and regularization
• Tree-based model feature selection metrics
• Using machine learning to select features
• A brief refresher on natural language processing
• Model-based feature selection
• Ranking the p-value
• Interpreting the p-value
• Feature selection using hypothesis testing
• Using Pearson correlation to select features
• Statistical-based feature selection
• The types of feature selection
• Creating a baseline machine learning pipeline
• A case study – a credit card defaulting dataset
• Achieving better performance in feature engineering
• Feature Selection
• Summary
• Using text in machine learning pipelines
• The Tf-idf vectorizer
• CountVectorizer parameters
• CountVectorizer
• Bag of words representation
• Text-specific feature construction
• Exploratory data analysis
• Parameters
• Polynomial features
• Activity recognition from the Single Chest-Mounted Accelerometer dataset
• Extending numerical features
• Creating our pipeline
• Bucketing continuous features into categories
• Encoding at the ordinal level
• Encoding at the nominal level
• Encoding categorical variables
• Custom quantitative imputer
• Custom category imputer
• Custom imputers
• Imputing categorical features
• Examining our dataset
• Feature Construction
• Summary
• Putting it all together
• The row normalization method
• The min-max scaling method
• Z-score standardization
• Standardization and normalization
• Pipelines in machine learning
• Imputing values in a machine learning pipeline
• Imputing the missing values in data
• Removing harmful rows of data
• Dealing with missing values in a dataset
• The exploratory data analysis (EDA)
• The Pima Indian Diabetes Prediction dataset
• Identifying missing values in data
• Feature Improvement - Cleaning Datasets
• Summary
• Recap of the levels of data
• Mathematical operations allowed
• The ratio level
• Plotting two columns at the interval level
• Mathematical operations allowed
• The interval level
• Mathematical operations allowed
• The ordinal level
• Mathematical operations allowed
• The nominal level
• The four levels of data
• Salary ranges by job classification
• Quantitative versus qualitative data
• An example of unstructured data – server logs
• The structure or lack thereof of data
• Feature Understanding – What's in My Dataset?
• Summary
• Feature learning – using AI to better our AI
• Feature transformation – enter math-man
• Feature construction – can we build it?
• Feature selection – say no to bad attributes
• Feature improvement – cleaning datasets
• Feature understanding – what’s in my dataset?
• Evaluating unsupervised learning algorithms
• Evaluating supervised learning algorithms
• Steps to evaluate a feature engineering procedure
• Example of feature engineering procedures – can anyone really predict the weather?
• Evaluation of machine learning algorithms and feature engineering procedures
• Unsupervised learning example – marketing segments
• Unsupervised learning
• Supervised learning
• Understanding the basics of data and machine learning
• What is feature engineering?
• Why feature engineering matters
• Motivating example – AI-powered communications
• Introduction to Feature Engineering
• Reviews
• Get in touch
• Conventions used
• Download the color images
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewer
• About the authors
• Contributors
• PacktPub.com
• Why subscribe?
• Packt Upsell
• 版權信息
• 封面

• 封面
• 版權信息
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Contributors
• About the authors
• About the reviewer
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Download the color images
• Conventions used
• Get in touch
• Reviews
• Introduction to Feature Engineering
• Motivating example – AI-powered communications
• Why feature engineering matters
• What is feature engineering?
• Understanding the basics of data and machine learning
• Supervised learning
• Unsupervised learning
• Unsupervised learning example – marketing segments
• Evaluation of machine learning algorithms and feature engineering procedures
• Example of feature engineering procedures – can anyone really predict the weather?
• Steps to evaluate a feature engineering procedure
• Evaluating supervised learning algorithms
• Evaluating unsupervised learning algorithms
• Feature understanding – what’s in my dataset?
• Feature improvement – cleaning datasets
• Feature selection – say no to bad attributes
• Feature construction – can we build it?
• Feature transformation – enter math-man
• Feature learning – using AI to better our AI
• Summary
• Feature Understanding – What's in My Dataset?
• The structure or lack thereof of data
• An example of unstructured data – server logs
• Quantitative versus qualitative data
• Salary ranges by job classification
• The four levels of data
• The nominal level
• Mathematical operations allowed
• The ordinal level
• Mathematical operations allowed
• The interval level
• Mathematical operations allowed
• Plotting two columns at the interval level
• The ratio level
• Mathematical operations allowed
• Recap of the levels of data
• Summary
• Feature Improvement - Cleaning Datasets
• Identifying missing values in data
• The Pima Indian Diabetes Prediction dataset
• The exploratory data analysis (EDA)
• Dealing with missing values in a dataset
• Removing harmful rows of data
• Imputing the missing values in data
• Imputing values in a machine learning pipeline
• Pipelines in machine learning
• Standardization and normalization
• Z-score standardization
• The min-max scaling method
• The row normalization method
• Putting it all together
• Summary
• Feature Construction
• Examining our dataset
• Imputing categorical features
• Custom imputers
• Custom category imputer
• Custom quantitative imputer
• Encoding categorical variables
• Encoding at the nominal level
• Encoding at the ordinal level
• Bucketing continuous features into categories
• Creating our pipeline
• Extending numerical features
• Activity recognition from the Single Chest-Mounted Accelerometer dataset
• Polynomial features
• Parameters
• Exploratory data analysis
• Text-specific feature construction
• Bag of words representation
• CountVectorizer
• CountVectorizer parameters
• The Tf-idf vectorizer
• Using text in machine learning pipelines
• Summary
• Feature Selection
• Achieving better performance in feature engineering
• A case study – a credit card defaulting dataset
• Creating a baseline machine learning pipeline
• The types of feature selection
• Statistical-based feature selection
• Using Pearson correlation to select features
• Feature selection using hypothesis testing
• Interpreting the p-value
• Ranking the p-value
• Model-based feature selection
• A brief refresher on natural language processing
• Using machine learning to select features
• Tree-based model feature selection metrics
• Linear models and regularization
• A brief introduction to regularization
• Linear model coefficients as another feature importance metric
• Choosing the right feature selection method
• Summary
• Feature Transformations
• Dimension reduction – feature transformations versus feature selection versus feature construction
• Principal Component Analysis
• How PCA works
• PCA with the Iris dataset – manual example
• Creating the covariance matrix of the dataset
• Calculating the eigenvalues of the covariance matrix
• Keeping the top k eigenvalues (sorted by the descending eigenvalues)
• Using the kept eigenvectors to transform new data-points
• Scikit-learn's PCA
• How centering and scaling data affects PCA
• A deeper look into the principal components
• Linear Discriminant Analysis
• How LDA works
• Calculating the mean vectors of each class
• Calculating within-class and between-class scatter matrices
• Calculating eigenvalues and eigenvectors for SW-1SB
• Keeping the top k eigenvectors by ordering them by descending eigenvalues
• Using the top eigenvectors to project onto the new space
• How to use LDA in scikit-learn
• LDA versus PCA – iris dataset
• Summary
• Feature Learning
• Parametric assumptions of data
• Non-parametric fallacy
• The algorithms of this chapter
• Restricted Boltzmann Machines
• Not necessarily dimension reduction
• The graph of a Restricted Boltzmann Machine
• The restriction of a Boltzmann Machine
• Reconstructing the data
• MNIST dataset
• The BernoulliRBM
• Extracting PCA components from MNIST
• Extracting RBM components from MNIST
• Using RBMs in a machine learning pipeline
• Using a linear model on raw pixel values
• Using a linear model on extracted PCA components
• Using a linear model on extracted RBM components
• Learning text features – word vectorizations
• Word embeddings
• Two approaches to word embeddings - Word2vec and GloVe
• Word2Vec - another shallow neural network
• The gensim package for creating Word2vec embeddings
• Application of word embeddings - information retrieval
• Summary
• Case Studies
• Case study 1 - facial recognition
• Applications of facial recognition
• The data
• Some data exploration
• Applied facial recognition
• Case study 2 - predicting topics of hotel reviews data
• Applications of text clustering
• Hotel review data
• Exploration of the data
• The clustering model
• SVD versus PCA components
• Latent semantic analysis
• Summary
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-25 22:46:20