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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Books for leisure reading
• Machine learning books from Packt
• Movies
• Web-based machine learning services

• Leave a review - let other readers know what you think 更新時間：2021-07-02 19:27:06
• Other Books You May Enjoy
• Books for leisure reading
• Machine learning books from Packt
• Movies
• Web-based machine learning services
• Machine learning and deep learning links
• Courses on machine learning
• Courses on R
• Visualization libraries
• Notebooks
• Creating dashboards
• Languages and tools
• NoSQL products
• Big data resources
• External Data Science Resources
• Summary
• Characteristics of successful projects
• The human factor
• Silicon Valley and data science
• Ethical considerations
• Corporate big data and data science strategy
• Closing Thoughts on Big Data
• Summary
• Tutorial – using RStudio in the cloud
• Enterprise Big Data
• On-premises hardware
• Containers – Docker Kubernetes and Mesos
• Virtualization
• Cloud computing
• Enterprise infrastructure solutions
• Machine learning as a service
• Apache MADlib
• Command-line tools
• Datarobot
• H2O and Driverless AI
• Deep learning
• Spark
• OpenCV Caffe and others
• Python
• The R programming language
• Enterprise data science – machine learning and AI
• Other common databases
• MapD
• Brytlyt
• GPU databases
• Azure CosmosDB
• Google BigQuery and other cloud services
• Amazon Redshift Redshift Spectrum and Athena databases
• Cloud databases
• Neo4j
• Cassandra
• MongoDB
• Kdb+
• Enterprise and open source NoSQL Databases
• SAP Hana
• Greenplum
• PostgreSQL
• IBM data warehouse systems (formerly Netezza appliances)
• Teradata
• HP Vertica
• Oracle Exadata Exalytics and TimesTen
• Traditional data warehouse systems
• Enterprise data warehouse and data mining
• Data science solutions in the enterprise
• A roadmap to enterprise analytics success
• Enterprise data science overview
• Enterprise Data Science
• Summary
• Running the application
• Using custom CSS and fonts for the application
• Shiny (R Code)
• Writing the R code for Apriori
• Downloading the data
• Tutorial - associative rules mining with CMS data
• The neural networks related algorithms
• The K-Means machine learning technique
• Support vector machines
• Boosting algorithms
• The Random forest extension
• Decision trees
• Lift
• Support
• Confidence
• Association rules
• Regression models
• Popular machine learning algorithms
• The gradient descent and VC Dimension theories
• The bias variance and regularization properties
• Machine Learning Deep Dive
• Summary
• Leveraging multicore processing in the model
• Creating the model
• The cross-validation parameter
• Splitting the data into train and test sets
• The train test splits and cross-validation concepts
• The importance of variables
• Data imputation
• Data sampling
• Other common data transformations
• Removing correlated variables
• The near-zero variance function
• Centering and scaling
• Pre-processing and feature selection techniques
• Data management steps in machine learning
• The core concepts in machine learning
• Common terminologies in machine learning
• Subdividing supervised machine learning
• Unsupervised machine learning
• Vehicle Mileage Number Recognition and other examples
• Supervised machine learning
• Supervised and unsupervised machine learning
• Categories of machine learning
• Machine learning statistics and AI
• Factors that led to the success of machine learning
• The evolution of machine learning
• What is machine learning?
• An Introduction to Machine Learning Concepts
• Summary
• Spark exercise - hands-on with Spark (Databricks)
• Signing up for Databricks Community Edition
• Spark practicals
• Spark solutions
• The architecture of Spark
• MLlib
• GraphX
• Spark Streaming
• Spark SQL
• Spark Core
• Core components in Spark
• Spark APIs
• Spark deployment options
• Actions and transformations
• Spark DataFrames
• SparkContext
• Directed acyclic graphs
• Resilient distributed datasets
• Theoretical concepts in Spark
• Overcoming the limitations of Hadoop
• Limitations of Hadoop
• The advent of Spark
• Spark for Big Data Analytics
• Summary
• Applications
• Putting it all together - The CMS Open Payments application
• R Shiny platform for developers
• Creating the frontend web portal
• The backend code
• Loading the data
• Creating the Q application
• Downloading the CMS Open Payments data
• The CMS Open Payments Portal
• Installing RStudio
• Installing R
• Installing kdb+
• Installing kdb+ R and RStudio
• Tracking physician payments with real-world data
• Installing and using MongoDB
• JSON format
• Analyzing Nobel Laureates data with MongoDB
• Other NoSQL types and summary of other types of databases
• Graph databases
• Key-value databases
• Document-oriented databases
• Columnar databases
• In-memory databases
• NoSQL databases
• Amazon Dynamo
• Google Bigtable
• The need for NoSQL technologies
• The CAP theorem
• The BASE property of NoSQL
• ACID and SQL
• The ACID BASE and CAP properties
• Why NoSQL?
• Big Data Mining with NoSQL
• Summary
• Joining tables in Hive
• Analyzing oil import prices with Hive
• WordCount using Hadoop MapReduce
• Hands-on with CDH
• The Hadoop ecosystem
• New features expected in Hadoop 3
• Hadoop data storage formats
• User authentication
• Encryption
• Other topics in Hadoop
• Job scheduling in YARN
• Hadoop YARN
• Block size and number of mappers and reducers
• A technical understanding of MapReduce
• An intuitive introduction to MapReduce
• Hadoop MapReduce
• Data storage process in HDFS
• Hadoop Distributed File System - HDFS
• The core modules of Hadoop
• The fundamental premise of Hadoop
• The fundamentals of Hadoop
• Big Data With Hadoop
• Summary
• Installing Python
• Installing RStudio
• Steps for downloading and installing Microsoft R Open
• Installing R
• Installing Spark
• Installing Packt Data Science Box
• Installing CDH in other environments
• Installing Oracle VirtualBox
• Installing Hadoop
• Installing on the cloud
• Installing on a laptop or workstation
• System recommendations
• Components of the Analytics Toolkit
• The Analytics Toolkit
• Summary
• Selection of the software stack
• Selection of the hardware stack
• Technical elements of the big data platform
• Implementing the solution
• Stakeholders of the solution
• Implementation life cycle
• Building the case for a Big Data strategy
• Big data mining in the enterprise
• What is big data mining?
• Big Data Mining for the Masses
• Summary
• When do you know you have a big data problem and where do you start your search for the big data solution?
• The 4Vs of big data
• Sources of big data
• Semi-structured
• Unstructured
• Structured
• Types of Big Data
• Building blocks of big data analytics
• Definition of big data
• Why we are talking about big data now if data has always existed
• From magnetic devices to SSDs
• The advent of the stored-program computer
• Dr. Alan Turing and modern computing
• Dawn of the information age
• A brief history of data
• What is big data?
• Too Big or Not Too Big
• 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 author
• 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 author
• 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
• Too Big or Not Too Big
• What is big data?
• A brief history of data
• Dawn of the information age
• Dr. Alan Turing and modern computing
• The advent of the stored-program computer
• From magnetic devices to SSDs
• Why we are talking about big data now if data has always existed
• Definition of big data
• Building blocks of big data analytics
• Types of Big Data
• Structured
• Unstructured
• Semi-structured
• Sources of big data
• The 4Vs of big data
• When do you know you have a big data problem and where do you start your search for the big data solution?
• Summary
• Big Data Mining for the Masses
• What is big data mining?
• Big data mining in the enterprise
• Building the case for a Big Data strategy
• Implementation life cycle
• Stakeholders of the solution
• Implementing the solution
• Technical elements of the big data platform
• Selection of the hardware stack
• Selection of the software stack
• Summary
• The Analytics Toolkit
• Components of the Analytics Toolkit
• System recommendations
• Installing on a laptop or workstation
• Installing on the cloud
• Installing Hadoop
• Installing Oracle VirtualBox
• Installing CDH in other environments
• Installing Packt Data Science Box
• Installing Spark
• Installing R
• Steps for downloading and installing Microsoft R Open
• Installing RStudio
• Installing Python
• Summary
• Big Data With Hadoop
• The fundamentals of Hadoop
• The fundamental premise of Hadoop
• The core modules of Hadoop
• Hadoop Distributed File System - HDFS
• Data storage process in HDFS
• Hadoop MapReduce
• An intuitive introduction to MapReduce
• A technical understanding of MapReduce
• Block size and number of mappers and reducers
• Hadoop YARN
• Job scheduling in YARN
• Other topics in Hadoop
• Encryption
• User authentication
• Hadoop data storage formats
• New features expected in Hadoop 3
• The Hadoop ecosystem
• Hands-on with CDH
• WordCount using Hadoop MapReduce
• Analyzing oil import prices with Hive
• Joining tables in Hive
• Summary
• Big Data Mining with NoSQL
• Why NoSQL?
• The ACID BASE and CAP properties
• ACID and SQL
• The BASE property of NoSQL
• The CAP theorem
• The need for NoSQL technologies
• Google Bigtable
• Amazon Dynamo
• NoSQL databases
• In-memory databases
• Columnar databases
• Document-oriented databases
• Key-value databases
• Graph databases
• Other NoSQL types and summary of other types of databases
• Analyzing Nobel Laureates data with MongoDB
• JSON format
• Installing and using MongoDB
• Tracking physician payments with real-world data
• Installing kdb+ R and RStudio
• Installing kdb+
• Installing R
• Installing RStudio
• The CMS Open Payments Portal
• Downloading the CMS Open Payments data
• Creating the Q application
• Loading the data
• The backend code
• Creating the frontend web portal
• R Shiny platform for developers
• Putting it all together - The CMS Open Payments application
• Applications
• Summary
• Spark for Big Data Analytics
• The advent of Spark
• Limitations of Hadoop
• Overcoming the limitations of Hadoop
• Theoretical concepts in Spark
• Resilient distributed datasets
• Directed acyclic graphs
• SparkContext
• Spark DataFrames
• Actions and transformations
• Spark deployment options
• Spark APIs
• Core components in Spark
• Spark Core
• Spark SQL
• Spark Streaming
• GraphX
• MLlib
• The architecture of Spark
• Spark solutions
• Spark practicals
• Signing up for Databricks Community Edition
• Spark exercise - hands-on with Spark (Databricks)
• Summary
• An Introduction to Machine Learning Concepts
• What is machine learning?
• The evolution of machine learning
• Factors that led to the success of machine learning
• Machine learning statistics and AI
• Categories of machine learning
• Supervised and unsupervised machine learning
• Supervised machine learning
• Vehicle Mileage Number Recognition and other examples
• Unsupervised machine learning
• Subdividing supervised machine learning
• Common terminologies in machine learning
• The core concepts in machine learning
• Data management steps in machine learning
• Pre-processing and feature selection techniques
• Centering and scaling
• The near-zero variance function
• Removing correlated variables
• Other common data transformations
• Data sampling
• Data imputation
• The importance of variables
• The train test splits and cross-validation concepts
• Splitting the data into train and test sets
• The cross-validation parameter
• Creating the model
• Leveraging multicore processing in the model
• Summary
• Machine Learning Deep Dive
• The bias variance and regularization properties
• The gradient descent and VC Dimension theories
• Popular machine learning algorithms
• Regression models
• Association rules
• Confidence
• Support
• Lift
• Decision trees
• The Random forest extension
• Boosting algorithms
• Support vector machines
• The K-Means machine learning technique
• The neural networks related algorithms
• Tutorial - associative rules mining with CMS data
• Downloading the data
• Writing the R code for Apriori
• Shiny (R Code)
• Using custom CSS and fonts for the application
• Running the application
• Summary
• Enterprise Data Science
• Enterprise data science overview
• A roadmap to enterprise analytics success
• Data science solutions in the enterprise
• Enterprise data warehouse and data mining
• Traditional data warehouse systems
• Oracle Exadata Exalytics and TimesTen
• HP Vertica
• Teradata
• IBM data warehouse systems (formerly Netezza appliances)
• PostgreSQL
• Greenplum
• SAP Hana
• Enterprise and open source NoSQL Databases
• Kdb+
• MongoDB
• Cassandra
• Neo4j
• Cloud databases
• Amazon Redshift Redshift Spectrum and Athena databases
• Google BigQuery and other cloud services
• Azure CosmosDB
• GPU databases
• Brytlyt
• MapD
• Other common databases
• Enterprise data science – machine learning and AI
• The R programming language
• Python
• OpenCV Caffe and others
• Spark
• Deep learning
• H2O and Driverless AI
• Datarobot
• Command-line tools
• Apache MADlib
• Machine learning as a service
• Enterprise infrastructure solutions
• Cloud computing
• Virtualization
• Containers – Docker Kubernetes and Mesos
• On-premises hardware
• Enterprise Big Data
• Tutorial – using RStudio in the cloud
• Summary
• Closing Thoughts on Big Data
• Corporate big data and data science strategy
• Ethical considerations
• Silicon Valley and data science
• The human factor
• Characteristics of successful projects
• Summary
• External Data Science Resources
• Big data resources
• NoSQL products
• Languages and tools
• Creating dashboards
• Notebooks
• Visualization libraries
• Courses on R
• Courses on machine learning
• Machine learning and deep learning links
• Web-based machine learning services
• Movies
• Machine learning books from Packt
• Books for leisure reading
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-07-02 19:27:06