最新章節

• Summary
• StumbleUponExecutor
• Machine learning pipeline with an example
• How pipelines work
• Estimators
• Transformers

• Summary 更新時間：2021-07-09 21:08:48
• StumbleUponExecutor
• Machine learning pipeline with an example
• How pipelines work
• Estimators
• Transformers
• Pipeline components
• DataFrames
• Introduction to pipelines
• Pipeline APIs for Spark ML
• Summary
• Structured Streaming
• Comparing model performance with Spark Streaming
• Online model evaluation
• Streaming K-means
• Creating a streaming regression model
• Creating a streaming data producer
• A simple streaming regression program
• Streaming regression
• Online learning with Spark Streaming
• Stateful streaming
• Streaming analytics
• Creating a basic streaming application
• The producer application
• Creating a basic streaming application
• Caching and fault tolerance with Spark Streaming
• Window operators
• Actions
• General transformations
• Keeping track of state
• Transformations
• Input sources
• An introduction to Spark Streaming
• Stream processing
• Online learning
• Real-Time Machine Learning with Spark Streaming
• Summary
• Word2Vec with Spark ML on the 20 Newsgroups dataset
• Word2Vec with Spark MLlib on the 20 Newsgroups dataset
• Word2Vec models
• Text classification with Spark 2.0
• Comparing raw features with processed tf-idf features on the 20 Newsgroups dataset
• Evaluating the impact of text processing
• Training a text classifier on the 20 Newsgroups dataset using tf-idf
• Document similarity with the 20 Newsgroups dataset and tf-idf features
• Using a tf-idf model
• Analyzing the tf-idf weightings
• Building a tf-idf model
• Feature Hashing
• A note about stemming
• Excluding terms based on frequency
• Removing stop words
• Improving our tokenization
• Applying basic tokenization
• Exploring the 20 Newsgroups data
• Extracting the tf-idf features from the 20 Newsgroups dataset
• Feature hashing
• Term weighting schemes
• Extracting the right features from your data
• What's so special about text data?
• Advanced Text Processing with Spark
• Summary
• Singular values
• Evaluating k for SVD on the LFW dataset
• Evaluating dimensionality reduction models
• The relationship between PCA and SVD
• Projecting data using PCA on the LFW dataset
• Using a dimensionality reduction model
• Interpreting the Eigenfaces
• Visualizing the Eigenfaces
• Running PCA on the LFW dataset
• Training a dimensionality reduction model
• Normalization
• Extracting feature vectors
• Converting to grayscale and resizing the images
• Loading images
• Extracting facial images as vectors
• Visualizing the face data
• Exploring the face data
• Extracting features from the LFW dataset
• Extracting the right features from your data
• Clustering as dimensionality reduction
• Relationship with matrix factorization
• Singular value decomposition
• Principal components analysis
• Types of dimensionality reduction
• Dimensionality Reduction with Spark
• Summary
• GMM - effect of iterations on cluster boundaries
• Plotting the user and item data with GMM clustering
• Clustering using GMM
• Gaussian Mixture Model
• WSSSE and iterations
• Bisecting K-means - training a clustering model
• Bisecting KMeans
• Effect of iterations on WSSSE
• Computing performance metrics on the MovieLens dataset
• External evaluation metrics
• Internal evaluation metrics
• K-means - evaluating the performance of clustering models
• Interpreting the movie clusters
• Interpreting the movie clusters
• K-means - interpreting cluster predictions on the MovieLens dataset
• Training a clustering model on the MovieLens dataset
• K-means - training a clustering model
• Extracting features from the MovieLens dataset
• Extracting the right features from your data
• Hierarchical clustering
• Mixture models
• Initialization methods
• k-means clustering
• Types of clustering models
• Building a Clustering Model with Spark
• Summary
• MaxBins
• Iterations
• The impact of parameter settings for the Gradient Boosted Trees
• Maximum bins
• Tree depth
• The impact of parameter settings for the decision tree
• Intercept
• L1 regularization
• L2 regularization
• Step size
• Iterations
• The impact of parameter settings for linear models
• Splitting data for Decision tree
• Creating training and testing sets to evaluate parameters
• Tuning model parameters
• Impact of training on log-transformed targets
• Transforming the target variable
• Improving model performance and tuning parameters
• Gradient boosted tree regression
• Random forest regression
• Ensembles of trees
• Decision tree regression
• Generalized linear regression
• Training a regression model on the bike sharing dataset
• BikeSharingExecutor
• Training and using regression models
• Extracting features from the bike sharing dataset
• Extracting the right features from your data
• The R-squared coefficient
• Root Mean Squared Log Error
• Mean Absolute Error
• Mean Squared Error and Root Mean Squared Error
• Evaluating the performance of regression models
• Decision trees for regression
• Least squares regression
• Types of regression models
• Building a Regression Model with Spark
• Summary
• Cross-validation
• The naive Bayes model
• Tuning tree depth and impurity
• Decision trees
• Regularization
• Step size
• Iterations
• Linear models
• Tuning model parameters
• Using the correct form of data
• Additional features
• Feature standardization
• Improving model performance and tuning parameters
• ROC curve and AUC
• Precision and recall
• Accuracy and prediction error
• Evaluating the performance of classification models
• Generating predictions for the Kaggle/StumbleUpon evergreen classification dataset
• Using classification models
• Training a classification model on the Kaggle/StumbleUpon evergreen classification dataset
• Training classification models
• Extracting the right features from your data
• Multilayer perceptron classifier
• Gradient-Boosted Trees
• Random Forests
• Ensembles of trees
• Decision trees
• The naive Bayes model
• Linear support vector machines
• StumbleUponExecutor
• Extracting features from the Kaggle/StumbleUpon evergreen classification dataset
• Visualizing the StumbleUpon dataset
• Multinomial logistic regression
• Logistic regression
• Linear models
• Types of classification models
• Building a Classification Model with Spark
• Summary
• FP-Growth Applied to Movie Lens Data
• FP-Growth Basic Sample
• FP-Growth algorithm
• MAP
• RMSE and MSE
• Using MLlib's built-in evaluation functions
• Mean Average Precision at K
• Mean Squared Error
• ALS Model Evaluation
• Evaluating the performance of recommendation models
• Inspecting the similar items
• Generating similar movies for the MovieLens 100k dataset
• Item recommendations
• Inspecting the recommendations
• Generating movie recommendations from the MovieLens 100k dataset
• User recommendations
• ALS Model recommendations
• Using the recommendation model
• Training a model using Implicit feedback data
• Training a model on the MovieLens 100k dataset
• Training the recommendation model
• Extracting features from the MovieLens 100k dataset
• Extracting the right features from your data
• Alternating least squares
• Basic model for Matrix Factorization
• Implicit Matrix Factorization
• Explicit matrix factorization
• Matrix factorization
• Collaborative filtering
• Content-based filtering
• Types of recommendation models
• Building a Recommendation Engine with Spark
• Summary
• Standard scalar
• Skip-gram model
• Word2Vector
• IDF
• TFID
• Using packages for feature extraction
• Using ML for feature normalization
• Normalizing features
• Sparse Vectors from Titles
• Simple text feature extraction
• Text features
• Extract time of day
• Transforming timestamps into categorical features
• Derived features
• Categorical features
• Numerical features
• Extracting useful features from your data
• Filling in bad or missing data
• Processing and transforming your data
• Distribution of number ratings
• Rating count bar chart
• Exploring the rating dataset
• Movie dataset
• Count by occupation
• Exploring the user dataset
• Exploring and visualizing your data
• The MovieLens 100k dataset
• Accessing publicly available datasets
• Obtaining Processing and Preparing Data with Spark
• Summary
• Spark 1.6 to 2.0
• MLlib versions compared
• MLlib vision
• Spark Integration
• MLlib supported methods and developer APIs
• Regression
• Clustering
• Classification
• Comparing algorithms supported by MLlib
• Performance improvements in Spark ML over Spark MLlib
• Spark MLlib
• An architecture for a machine learning system
• Data Pipeline in Apache Spark
• Batch versus real time
• Model monitoring and feedback
• Model deployment and integration
• Model training and testing loop
• Data cleansing and transformation
• Data ingestion and storage
• The components of a data-driven machine learning system
• Types of machine learning models
• Predictive modeling and analytics
• Targeted marketing and customer segmentation
• Personalization
• Business use cases for a machine learning system
• Introducing MovieStream
• What is Machine Learning?
• Designing a Machine Learning System
• Summary
• Plotting
• Lagranges multipliers
• Integral calculus
• Differential calculus
• Calculus
• Prior likelihood and posterior
• Gradient descent
• Hypothesis
• Functional composition
• Function types
• Functions
• Matrices in machine learning
• Singular value decomposition
• Eigenvalues and eigenvectors
• Determinant
• Matrix operations
• Distributed matrix in Spark
• Matrix in Spark
• Types of matrices
• Matrix
• Vectors in machine learning
• Hyperplanes
• Vector operations
• Vectors in Spark
• Vectors in Breeze
• Vector types
• Vector spaces
• Vectors
• Complex numbers
• Real numbers
• Fields
• Setting up the Scala environment on the Command Line
• Setting up the Scala environment in Intellij
• Linear algebra
• Math for Machine Learning
• Summary
• Submitting a Job
• Creating a Cluster
• Hadoop and Spark Versions
• Spark Cluster on Google Compute Engine - DataProc
• Benefits of using Spark ML as compared to existing libraries
• Supported machine learning algorithms by Spark
• UI in Spark
• Configuring and running Spark on Amazon Elastic Map Reduce
• Launching an EC2 Spark cluster
• Getting Spark running on Amazon EC2
• SparkR DataFrames
• The first step to a Spark program in R
• The first step to a Spark program in Python
• The first step to a Spark program in Java
• The first step to a Spark program in Scala
• Spark data frame
• SchemaRDD
• Broadcast variables and accumulators
• Caching RDDs
• Spark operations
• Creating RDDs
• Resilient Distributed Datasets
• The Spark shell
• SparkSession
• SparkContext and SparkConf
• The Spark programming model
• Spark clusters
• Installing and setting up Spark locally
• Getting Up and Running with Spark
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• 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 Authors
• Credits
• Copyright
• Title Page
• coverpage

• coverpage
• Title Page
• Copyright
• Credits
• About the Authors
• 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
• Downloading the color images of this book
• Errata
• Piracy
• Questions
• Getting Up and Running with Spark
• Installing and setting up Spark locally
• Spark clusters
• The Spark programming model
• SparkContext and SparkConf
• SparkSession
• The Spark shell
• Resilient Distributed Datasets
• Creating RDDs
• Spark operations
• Caching RDDs
• Broadcast variables and accumulators
• SchemaRDD
• Spark data frame
• The first step to a Spark program in Scala
• The first step to a Spark program in Java
• The first step to a Spark program in Python
• The first step to a Spark program in R
• SparkR DataFrames
• Getting Spark running on Amazon EC2
• Launching an EC2 Spark cluster
• Configuring and running Spark on Amazon Elastic Map Reduce
• UI in Spark
• Supported machine learning algorithms by Spark
• Benefits of using Spark ML as compared to existing libraries
• Spark Cluster on Google Compute Engine - DataProc
• Hadoop and Spark Versions
• Creating a Cluster
• Submitting a Job
• Summary
• Math for Machine Learning
• Linear algebra
• Setting up the Scala environment in Intellij
• Setting up the Scala environment on the Command Line
• Fields
• Real numbers
• Complex numbers
• Vectors
• Vector spaces
• Vector types
• Vectors in Breeze
• Vectors in Spark
• Vector operations
• Hyperplanes
• Vectors in machine learning
• Matrix
• Types of matrices
• Matrix in Spark
• Distributed matrix in Spark
• Matrix operations
• Determinant
• Eigenvalues and eigenvectors
• Singular value decomposition
• Matrices in machine learning
• Functions
• Function types
• Functional composition
• Hypothesis
• Gradient descent
• Prior likelihood and posterior
• Calculus
• Differential calculus
• Integral calculus
• Lagranges multipliers
• Plotting
• Summary
• Designing a Machine Learning System
• What is Machine Learning?
• Introducing MovieStream
• Business use cases for a machine learning system
• Personalization
• Targeted marketing and customer segmentation
• Predictive modeling and analytics
• Types of machine learning models
• The components of a data-driven machine learning system
• Data ingestion and storage
• Data cleansing and transformation
• Model training and testing loop
• Model deployment and integration
• Model monitoring and feedback
• Batch versus real time
• Data Pipeline in Apache Spark
• An architecture for a machine learning system
• Spark MLlib
• Performance improvements in Spark ML over Spark MLlib
• Comparing algorithms supported by MLlib
• Classification
• Clustering
• Regression
• MLlib supported methods and developer APIs
• Spark Integration
• MLlib vision
• MLlib versions compared
• Spark 1.6 to 2.0
• Summary
• Obtaining Processing and Preparing Data with Spark
• Accessing publicly available datasets
• The MovieLens 100k dataset
• Exploring and visualizing your data
• Exploring the user dataset
• Count by occupation
• Movie dataset
• Exploring the rating dataset
• Rating count bar chart
• Distribution of number ratings
• Processing and transforming your data
• Filling in bad or missing data
• Extracting useful features from your data
• Numerical features
• Categorical features
• Derived features
• Transforming timestamps into categorical features
• Extract time of day
• Text features
• Simple text feature extraction
• Sparse Vectors from Titles
• Normalizing features
• Using ML for feature normalization
• Using packages for feature extraction
• TFID
• IDF
• Word2Vector
• Skip-gram model
• Standard scalar
• Summary
• Building a Recommendation Engine with Spark
• Types of recommendation models
• Content-based filtering
• Collaborative filtering
• Matrix factorization
• Explicit matrix factorization
• Implicit Matrix Factorization
• Basic model for Matrix Factorization
• Alternating least squares
• Extracting the right features from your data
• Extracting features from the MovieLens 100k dataset
• Training the recommendation model
• Training a model on the MovieLens 100k dataset
• Training a model using Implicit feedback data
• Using the recommendation model
• ALS Model recommendations
• User recommendations
• Generating movie recommendations from the MovieLens 100k dataset
• Inspecting the recommendations
• Item recommendations
• Generating similar movies for the MovieLens 100k dataset
• Inspecting the similar items
• Evaluating the performance of recommendation models
• ALS Model Evaluation
• Mean Squared Error
• Mean Average Precision at K
• Using MLlib's built-in evaluation functions
• RMSE and MSE
• MAP
• FP-Growth algorithm
• FP-Growth Basic Sample
• FP-Growth Applied to Movie Lens Data
• Summary
• Building a Classification Model with Spark
• Types of classification models
• Linear models
• Logistic regression
• Multinomial logistic regression
• Visualizing the StumbleUpon dataset
• Extracting features from the Kaggle/StumbleUpon evergreen classification dataset
• StumbleUponExecutor
• Linear support vector machines
• The naive Bayes model
• Decision trees
• Ensembles of trees
• Random Forests
• Gradient-Boosted Trees
• Multilayer perceptron classifier
• Extracting the right features from your data
• Training classification models
• Training a classification model on the Kaggle/StumbleUpon evergreen classification dataset
• Using classification models
• Generating predictions for the Kaggle/StumbleUpon evergreen classification dataset
• Evaluating the performance of classification models
• Accuracy and prediction error
• Precision and recall
• ROC curve and AUC
• Improving model performance and tuning parameters
• Feature standardization
• Additional features
• Using the correct form of data
• Tuning model parameters
• Linear models
• Iterations
• Step size
• Regularization
• Decision trees
• Tuning tree depth and impurity
• The naive Bayes model
• Cross-validation
• Summary
• Building a Regression Model with Spark
• Types of regression models
• Least squares regression
• Decision trees for regression
• Evaluating the performance of regression models
• Mean Squared Error and Root Mean Squared Error
• Mean Absolute Error
• Root Mean Squared Log Error
• The R-squared coefficient
• Extracting the right features from your data
• Extracting features from the bike sharing dataset
• Training and using regression models
• BikeSharingExecutor
• Training a regression model on the bike sharing dataset
• Generalized linear regression
• Decision tree regression
• Ensembles of trees
• Random forest regression
• Gradient boosted tree regression
• Improving model performance and tuning parameters
• Transforming the target variable
• Impact of training on log-transformed targets
• Tuning model parameters
• Creating training and testing sets to evaluate parameters
• Splitting data for Decision tree
• The impact of parameter settings for linear models
• Iterations
• Step size
• L2 regularization
• L1 regularization
• Intercept
• The impact of parameter settings for the decision tree
• Tree depth
• Maximum bins
• The impact of parameter settings for the Gradient Boosted Trees
• Iterations
• MaxBins
• Summary
• Building a Clustering Model with Spark
• Types of clustering models
• k-means clustering
• Initialization methods
• Mixture models
• Hierarchical clustering
• Extracting the right features from your data
• Extracting features from the MovieLens dataset
• K-means - training a clustering model
• Training a clustering model on the MovieLens dataset
• K-means - interpreting cluster predictions on the MovieLens dataset
• Interpreting the movie clusters
• Interpreting the movie clusters
• K-means - evaluating the performance of clustering models
• Internal evaluation metrics
• External evaluation metrics
• Computing performance metrics on the MovieLens dataset
• Effect of iterations on WSSSE
• Bisecting KMeans
• Bisecting K-means - training a clustering model
• WSSSE and iterations
• Gaussian Mixture Model
• Clustering using GMM
• Plotting the user and item data with GMM clustering
• GMM - effect of iterations on cluster boundaries
• Summary
• Dimensionality Reduction with Spark
• Types of dimensionality reduction
• Principal components analysis
• Singular value decomposition
• Relationship with matrix factorization
• Clustering as dimensionality reduction
• Extracting the right features from your data
• Extracting features from the LFW dataset
• Exploring the face data
• Visualizing the face data
• Extracting facial images as vectors
• Loading images
• Converting to grayscale and resizing the images
• Extracting feature vectors
• Normalization
• Training a dimensionality reduction model
• Running PCA on the LFW dataset
• Visualizing the Eigenfaces
• Interpreting the Eigenfaces
• Using a dimensionality reduction model
• Projecting data using PCA on the LFW dataset
• The relationship between PCA and SVD
• Evaluating dimensionality reduction models
• Evaluating k for SVD on the LFW dataset
• Singular values
• Summary
• Advanced Text Processing with Spark
• What's so special about text data?
• Extracting the right features from your data
• Term weighting schemes
• Feature hashing
• Extracting the tf-idf features from the 20 Newsgroups dataset
• Exploring the 20 Newsgroups data
• Applying basic tokenization
• Improving our tokenization
• Removing stop words
• Excluding terms based on frequency
• A note about stemming
• Feature Hashing
• Building a tf-idf model
• Analyzing the tf-idf weightings
• Using a tf-idf model
• Document similarity with the 20 Newsgroups dataset and tf-idf features
• Training a text classifier on the 20 Newsgroups dataset using tf-idf
• Evaluating the impact of text processing
• Comparing raw features with processed tf-idf features on the 20 Newsgroups dataset
• Text classification with Spark 2.0
• Word2Vec models
• Word2Vec with Spark MLlib on the 20 Newsgroups dataset
• Word2Vec with Spark ML on the 20 Newsgroups dataset
• Summary
• Real-Time Machine Learning with Spark Streaming
• Online learning
• Stream processing
• An introduction to Spark Streaming
• Input sources
• Transformations
• Keeping track of state
• General transformations
• Actions
• Window operators
• Caching and fault tolerance with Spark Streaming
• Creating a basic streaming application
• The producer application
• Creating a basic streaming application
• Streaming analytics
• Stateful streaming
• Online learning with Spark Streaming
• Streaming regression
• A simple streaming regression program
• Creating a streaming data producer
• Creating a streaming regression model
• Streaming K-means
• Online model evaluation
• Comparing model performance with Spark Streaming
• Structured Streaming
• Summary
• Pipeline APIs for Spark ML
• Introduction to pipelines
• DataFrames
• Pipeline components
• Transformers
• Estimators
• How pipelines work
• Machine learning pipeline with an example
• StumbleUponExecutor
• Summary 更新時間：2021-07-09 21:08:48